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سامانه پیشنهادگر ترکیبی، مبتنی بر هستان‌شناسی برای مقابله با مشکل شروع سرد An Ontological Hybrid Recommender System for Dealing with Cold Start Problem 1 1 انتظار می‌­رود سامانه‌های پیشنهاد­گر (RS) قلم‌های دقیق را به مصرف‌کنندگان پیشنهاد دهند. شروع سرد مهم‌­ترین چالش در RS‌ها است. RS‌های ترکیبی اخیر، دو مدل پالایش محتوا پایه  (ConF)و پالایش مشارکتی (ColF) را با هم ترکیب می­‌کنند. در این پژوهش، یک RS ترکیبی مبتنی بر هستان‌شناسی معرفی می‌­شود که در آن هستان­‌شناسی در بخش ConF به‌کار رفته است، این در حالی است که ساختار هستان­‌شناسی توسط بخش ColF بهبود داده می‌­شود. در این مقاله، رویکرد ترکیبی جدیدی مبتنی بر ترکیب شباهت جمعیت‌شناختی و شباهت کسینوسی بین کاربران به‌­منظور حل مشکل شروع سرد از نوع کاربر جدید، ارائه شده است. همچنین، رویکرد جدیدی مبتنی بر ترکیب شباهت هستان­شناسی و شباهت کسینوسی بین اقلام به‌منظور حل مسأله شروع سرد از نوع قلم جدید، ارائه شده است. ایده اصلی روش پیشنهادی، گسترش پروفایل‌های کاربر/‌قلم بر اساس سازوکارهای مختلف برای ایجاد پروفایل با عملکرد بالاتر برای کاربران/قلم‌­ها است. روش پیشنهادی در یک مجموعه‌داده واقعی ارزیابی شده است و آزمایش­‌ها نشان می­‌دهند که روش پیشنهادی در مقایسه با روش‌های پیشرفتهRS ، به‌خصوص در مواجهه با مسأله شروع سرد، عملکرد بهتری دارد. 2 Recommender systems that predict user ratings for a set of items are known as subset of information filtration systems. They help users find their favorite items from thousands of available items. One of the most important and challenging problems that recommendation systems suffer from is the problem of dispersion. This means that due to the scatter of data in the system, they are not able to find popular items with the desired reliability and accuracy. This is especially true when there are a large number of items and users in the system and the filled ratings are low. Another challenging problem that these systems suffer from is their scalability. One of the major problems with these systems is the cold start. This problem occurs due to the small number of items rated by the user, i.e. the scatter of users. This problem is divided into two categories: new user and new item. The main focus of this article is on the problem of the new user type. This problem occurs when a new user has just logged in and has not rated any item yet, or when the user has already logged in but has been less active in rating. The goal is to address these three challenges. In this study, an ontology-based hybrid recommender system is introduced in which ontology is used in the content-based filtering section, while the ontology structure is improved by the collaborative filtering section. In this paper, a new hybrid approach based on combining demographic similarity and cosine similarity between users is presented in order to solve the cold start problem of the new user type. Also, a new approach based on combining ontological similarity and cosine similarity between items is proposed to solve the cold start problem of the new item type. The main idea of the proposed method is to extend users’/items’ profiles based on different mechanisms to create higher-performance profiles for users/items. The proposed method is evaluated in a real data set, and experiments show that the proposed method performs better than the advanced recommender system methods, especially in the case of cold start. 1 18 2020/12/16 1399/9/26 2021/05/22 1400/3/1 پیام بحرانی payam bahrani گروه مهندسی کامپیوتر، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران ایران p.bahrani@gmail.com بهروز مینایی بیدگلی Behrouz Minaei Bidgoli دانشگاه علم و صنعت ایران ایران b_minaei-at@ac.ir حمید پروین Hamid Parvin دانشگاه آزاد اسلامی واحد نورآباد ممسنی، فارس، ایران ایران parvin@iust.ac.ir میترا میرزارضایی Mitra Mirzarezaee دانشگاه آزاد اسلامی واحد علوم و تحقیقات ایران mitra_mirzaee@gmail.com احمد کشاورز Ahmad Keshavarz گروه مهندسی برق، دانشگاه خلیج فارس، بوشهر، ایران ایران ahmad_keshavarz@gmail.com Recommender System Ontology Profile Expansion Hybrid Recommender System سامانه پیشنهاد‌گر هستان‌شناسی توسعه پروفایل سامانه پیشنهاد‌گر ترکیبی [1] R. Yera and L. Martinez, "Fuzzy tools in recommender systems: A survey," International Journal of Computational Intelligence Systems, vol. 10, pp. 776-803, 2017.##[2] M. D. Ekstrand and J. A. Konstan, "Recommender Systems Notation," 2019.##[3] M. Doja, "Recommender System for Personalized Adaptive E-learning Platforms to Enhance Learning Capabilities of Learners Based on their Learning Style and Knowledge Level," 2019.##[4] F. S. d. Aguiar Neto, "Pre-processing approaches for collaborative filtering based on hierarchical clustering," Universidade de São Paulo.##[5] M.-P. T. Do, D. V. Nguyen, and L. Nguyen, "Model-based Approach for Collaborative Filtering," 2019.##[6] Y. Yang, Y. Xu, E. Wang, J. Han, and Z. Yu, "Improving existing collaborative filtering recommendations via serendipity-based algorithm," IEEE Transactions on Multimedia, vol. 20, pp. 1888-1900, 2017.##[7] S. K. Raghuwanshi and R. Pateriya, "Collaborative Filtering Techniques in Recommendation Systems," in Data, Engineering and Applications, ed: Springer, 2019, pp. 11-21.##[8] T. N. Duong, V. D. Than, T. H. Tran, Q. H. Dang, D. M. Nguyen, and H. M. Pham, "An Effective Similarity Measure for Neighborhood-based Collaborative Filtering," in 2018 5th NAFOSTED Conference on Information and Computer Science (NICS), 2018, pp. 250-254.##[9] J. Feng, X. Fengs, N. Zhang, and J. Peng, "An improved collaborative filtering method based on similarity," PloS one, vol. 13, pp. e0204003, 2018.##[10] P. Thakkar, K. Varma, V. Ukani, S. Mankad, and S. Tanwar, "Combining User-Based and Item-Based Collaborative Filtering Using Machine Learning," in Information and Communication Technology for Intelligent Systems, ed: Springer, 2019, pp. 173-180.##[11] Z. Yang, C. Fu, R. Lin, T. Peng, and Y. Tang, "Collaborative Filtering Recommendation Algorithm Based on AdaBoost-Naïve Bayesian Algorithm," in International Conference on Human Centered Computing, 2018, pp. 380-392.##[12] B. S. Neysiani, N. Soltani, R. Mofidi, and M. H. Nadimi-Shahraki, "Improve Performance of Association Rule-Based Collaborative Filtering Recommendation Systems using Genetic Algorithm," 2019.##[13] J. Borràs, A. Moreno, and A. Valls, "Intelligent tourism recommender systems: A survey," Expert Systems with Applications, vol. 41, pp. 7370-7389, 2014.##[14] S. Gong and H. Ye, "An item based collaborative filtering using bp neural networks prediction," in 2009 International Conference on Industrial and Information Systems, 2009, pp. 146-148.##[15] A. Abdelwahab, H. Sekiya, I. Matsuba, Y. Horiuchi, S. Kuroiwa, and M. Nishida, "An efficient collaborative filtering algorithm using SVD-free Latent Semantic Indexing and particle swarm optimization," in 2009 International Conference on Natural Language Processing and Knowledge Engineering, 2009, pp. 1-4.##[16] I. Viktoratos, A. Tsadiras, and N. Bassiliades, "Combining community-based knowledge with association rule mining to alleviate the cold start problem in context-aware recommender systems," Expert Systems with Applications, vol. 101, pp. 78-90, 2018.##[17] Z. Li, H. Zhao, Q. Liu, Z. Huang, T. Mei, and E. Chen, "Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors," in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018, pp. 1734-1743.##[18] R. Logesh, V. Subramaniyaswamy, D. Malathi, N. Sivaramakrishnan, and V. Vijayakumar, "Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method," Neural Computing and Applications, pp. 1-24, 2019.##[19] Y. Qian, Y. Zhang, X. Ma, H. Yu, and L. Peng, "EARS: Emotion-aware recommender system based on hybrid information fusion," Information Fusion, vol. 46, pp. 141-146, 2019.##[20] T. Mohammadpour, A. M. Bidgoli, R. Enayatifar, and H. H. S. Javadi, "Efficient clustering in collaborative filtering recommender system: Hybrid method based on genetic algorithm and gravitational emulation local search algorithm," Genomics, 2019.##[21] P. Valdiviezo-Díaz and J. Bobadilla, "A Hybrid Approach of Recommendation via Extended Matrix Based on Collaborative Filtering with Demographics Information," in International Conference on Technology Trends, 2018, pp. 384-398.##[22] M. Batet, A. Moreno, D. Sánchez, D. Isern, and A. Valls, "Turist@: Agent-based personalised recommendation of tourist activities," Expert Systems with Applications, vol. 39, pp. 7319-7329, 2012.##[23] D. Kotkov, J. A. Konstan, Q. Zhao, and J. Veijalainen, "Investigating serendipity in recommender systems based on real user feedback," in Proceedings of the 33rd Annual ACM Symposium on Applied Computing, 2018, pp. 1341-1350.##[24] M. Eirinaki, J. Gao, I. Varlamis, and K. Tserpes, "Recommender systems for large-scale social networks: A review of challenges and solutions," ed: Elsevier, 2018.##[25] M. Y. H. Al-Shamri, "User profiling approaches for demographic recommender systems," Knowledge-Based Systems, vol. 100, pp. 175-187, 2016.##[26] L. Safoury and A. Salah, "Exploiting user demographic attributes for solving cold-start problem in recommender system," Lecture Notes on Software Engineering, vol. 1, pp. 303-307, 2013.##[27] M. M. Khan, R. Ibrahim, M. Younas, I. Ghani, and S. R. Jeong, "Facebook interactions utilization for addressing recommender systems cold start problem across system domain," Journal of Internet Technology, vol. 19, pp. 861-870, 2018.##[28] V. S. Dixit and P. Jain, "Recommendations with Sparsity Based Weighted Context Framework," in International Conference on Computational Science and Its Applications, 2018, pp. 289-305.##[29] H. J. Ahn, "A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem," Information Sciences, vol. 178, pp. 37-51, 2008.##[30] V. Formoso, D. FernáNdez, F. Cacheda, and V. Carneiro, "Using profile expansion techniques to alleviate the new user problem," Information processing & management, vol. 49, pp. 659-672, 2013.##[31] R. Attar and A. S. Fraenkel, "Local feedback in full-text retrieval systems," Journal of the ACM (JACM), vol. 24, pp. 397-417, 1977.##[32] A. M. Acilar and A. Arslan, "A collaborative filtering method based on artificial immune network," Expert Systems with Applications, vol. 36, pp. 8324-8332, 2009.##[33] G. Guo, "Improving the performance of recommender systems by alleviating the data sparsity and cold start problems," in Twenty-Third International Joint Conference on Artificial Intelligence, 2013.##[34] G. Shaw, Y. Xu, and S. Geva, "Using association rules to solve the cold-start problem in recommender systems," in Pacific-Asia conference on knowledge discovery and data mining, 2010, pp. 340-347.##[35] Q. Liu, E. Chen, H. Xiong, C. H. Ding, and J. Chen, "Enhancing collaborative filtering by user interest expansion via personalized ranking," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 42, pp. 218-233, 2011.##[36] J. Demšar, "Statistical comparisons of classifiers over multiple data sets," Journal of Machine learning research, vol. 7, pp. 1-30, 2006.##[37] G. Karypis, "Evaluation of item-based top-n recommendation algorithms," in Proceedings of the tenth international conference on Information and knowledge management, 2001, pp. 247-254.##[38] P. Cremonesi, Y. Koren, and R. Turrin, "Performance of recommender algorithms on top-n recommendation tasks," in Proceedings of the fourth ACM conference on Recommender systems, 2010, pp. 39-46.##[39] R. Bambini, P. Cremonesi, and R. Turrin, "A recommender system for an IPTV service provider: a real large-scale production environment," in Recommender systems handbook, ed: Springer, 2011, pp. 299-331.##[40] H. Cui, M. Zhu, and S. Yao, "Ontology-based Top-N Recommendations on new items with matrix factorization," Journal of Software, vol. 9, pp. 2026-2032, 2014.##[41] J.Zhong, , H. Xie, & F.L. Wang, "The research trends in recommender systems for e-learning: A systematic review of SSCI journal articles from 2014 to 2018", Asian Association of Open Universities Journal, vol.14(1), pp.12-27, 2019.##[42] V.Vanitha, P. Krishnan, "A modified ant colony algorithm for personalized learning path construction", Journal of Intelligent & Fuzzy Systems, vol. 37(5), pp. 6785-6800.##[43] L. H. Son, Dealing with the new user cold-start problem in recommender systems: A comparative review. Information Systems, vol.58, pp. 87-104, 2016.##[44] N.Silva, D.Carvalho, A. C.Pereira, F. Mourão, & L.Rocha, "The pure cold-start problem: A deep study about how to conquer first-time users in recommendations domains", Information Systems, vol. 80, pp. 1-12, 2019.##[45] L.Romero, C. Saucedo, M. L. Caliusco, & M.Gutiérrez, "Supporting self-regulated learning and personalization using ePortfolios: a semantic approach based on learning paths", International Journal of Educational Technology in Higher Education, vol.16(1), pp.16, 2019.##[46] J.R. Almeida, E.Monteiro, L.B.Silva, A.P.Sierra, J.L.Oliveira, "A Recommender System to Help Discovering Cohorts in Rare Diseases", In Proceedings of the 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Rochester, MN, USA, pp.28-30, July, 2020.##[1] R. Yera and L. Martinez, "Fuzzy tools in recommender systems: A survey," International Journal of Computational Intelligence Systems, vol. 10, pp. 776-803, 2017.##[2] M. D. Ekstrand and J. A. Konstan, "Recommender Systems Notation," 2019.##[3] M. Doja, "Recommender System for Personalized Adaptive E-learning Platforms to Enhance Learning Capabilities of Learners Based on their Learning Style and Knowledge Level," 2019.##[4] F. S. d. Aguiar Neto, "Pre-processing approaches for collaborative filtering based on hierarchical clustering," Universidade de São Paulo.##[5] M.-P. T. Do, D. V. Nguyen, and L. Nguyen, "Model-based Approach for Collaborative Filtering," 2019.##[6] Y. Yang, Y. Xu, E. Wang, J. Han, and Z. Yu, "Improving existing collaborative filtering recommendations via serendipity-based algorithm," IEEE Transactions on Multimedia, vol. 20, pp. 1888-1900, 2017.##[7] S. K. Raghuwanshi and R. Pateriya, "Collaborative Filtering Techniques in Recommendation Systems," in Data, Engineering and Applications, ed: Springer, 2019, pp. 11-21.##[8] T. N. Duong, V. D. Than, T. H. Tran, Q. H. Dang, D. M. Nguyen, and H. M. Pham, "An Effective Similarity Measure for Neighborhood-based Collaborative Filtering," in 2018 5th NAFOSTED Conference on Information and Computer Science (NICS), 2018, pp. 250-254.##[9] J. Feng, X. Fengs, N. Zhang, and J. Peng, "An improved collaborative filtering method based on similarity," PloS one, vol. 13, pp. e0204003, 2018.##[10] P. Thakkar, K. Varma, V. Ukani, S. Mankad, and S. Tanwar, "Combining User-Based and Item-Based Collaborative Filtering Using Machine Learning," in Information and Communication Technology for Intelligent Systems, ed: Springer, 2019, pp. 173-180.##[11] Z. Yang, C. Fu, R. Lin, T. Peng, and Y. Tang, "Collaborative Filtering Recommendation Algorithm Based on AdaBoost-Naïve Bayesian Algorithm," in International Conference on Human Centered Computing, 2018, pp. 380-392.##[12] B. S. Neysiani, N. Soltani, R. Mofidi, and M. H. Nadimi-Shahraki, "Improve Performance of Association Rule-Based Collaborative Filtering Recommendation Systems using Genetic Algorithm," 2019.##[13] J. Borràs, A. Moreno, and A. Valls, "Intelligent tourism recommender systems: A survey," Expert Systems with Applications, vol. 41, pp. 7370-7389, 2014.##[14] S. Gong and H. Ye, "An item based collaborative filtering using bp neural networks prediction," in 2009 International Conference on Industrial and Information Systems, 2009, pp. 146-148.##[15] A. Abdelwahab, H. Sekiya, I. Matsuba, Y. Horiuchi, S. Kuroiwa, and M. Nishida, "An efficient collaborative filtering algorithm using SVD-free Latent Semantic Indexing and particle swarm optimization," in 2009 International Conference on Natural Language Processing and Knowledge Engineering, 2009, pp. 1-4.##[16] I. Viktoratos, A. Tsadiras, and N. Bassiliades, "Combining community-based knowledge with association rule mining to alleviate the cold start problem in context-aware recommender systems," Expert Systems with Applications, vol. 101, pp. 78-90, 2018.##[17] Z. Li, H. Zhao, Q. Liu, Z. Huang, T. Mei, and E. Chen, "Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors," in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018, pp. 1734-1743.##[18] R. Logesh, V. Subramaniyaswamy, D. Malathi, N. Sivaramakrishnan, and V. Vijayakumar, "Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method," Neural Computing and Applications, pp. 1-24, 2019.##[19] Y. Qian, Y. Zhang, X. Ma, H. Yu, and L. Peng, "EARS: Emotion-aware recommender system based on hybrid information fusion," Information Fusion, vol. 46, pp. 141-146, 2019.##[20] T. Mohammadpour, A. M. Bidgoli, R. Enayatifar, and H. H. S. Javadi, "Efficient clustering in collaborative filtering recommender system: Hybrid method based on genetic algorithm and gravitational emulation local search algorithm," Genomics, 2019.##[21] P. Valdiviezo-Díaz and J. Bobadilla, "A Hybrid Approach of Recommendation via Extended Matrix Based on Collaborative Filtering with Demographics Information," in International Conference on Technology Trends, 2018, pp. 384-398.##[22] M. Batet, A. Moreno, D. Sánchez, D. Isern, and A. Valls, "Turist@: Agent-based personalised recommendation of tourist activities," Expert Systems with Applications, vol. 39, pp. 7319-7329, 2012.##[23] D. Kotkov, J. A. Konstan, Q. Zhao, and J. Veijalainen, "Investigating serendipity in recommender systems based on real user feedback," in Proceedings of the 33rd Annual ACM Symposium on Applied Computing, 2018, pp. 1341-1350.##[24] M. Eirinaki, J. Gao, I. Varlamis, and K. Tserpes, "Recommender systems for large-scale social networks: A review of challenges and solutions," ed: Elsevier, 2018.##[25] M. Y. H. Al-Shamri, "User profiling approaches for demographic recommender systems," Knowledge-Based Systems, vol. 100, pp. 175-187, 2016.##[26] L. Safoury and A. Salah, "Exploiting user demographic attributes for solving cold-start problem in recommender system," Lecture Notes on Software Engineering, vol. 1, pp. 303-307, 2013.##[27] M. M. Khan, R. Ibrahim, M. Younas, I. Ghani, and S. R. Jeong, "Facebook interactions utilization for addressing recommender systems cold start problem across system domain," Journal of Internet Technology, vol. 19, pp. 861-870, 2018.##[28] V. S. Dixit and P. Jain, "Recommendations with Sparsity Based Weighted Context Framework," in International Conference on Computational Science and Its Applications, 2018, pp. 289-305.##[29] H. J. Ahn, "A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem," Information Sciences, vol. 178, pp. 37-51, 2008.##[30] V. Formoso, D. FernáNdez, F. Cacheda, and V. Carneiro, "Using profile expansion techniques to alleviate the new user problem," Information processing & management, vol. 49, pp. 659-672, 2013.##[31] R. Attar and A. S. Fraenkel, "Local feedback in full-text retrieval systems," Journal of the ACM (JACM), vol. 24, pp. 397-417, 1977.##[32] A. M. Acilar and A. Arslan, "A collaborative filtering method based on artificial immune network," Expert Systems with Applications, vol. 36, pp. 8324-8332, 2009.##[33] G. Guo, "Improving the performance of recommender systems by alleviating the data sparsity and cold start problems," in Twenty-Third International Joint Conference on Artificial Intelligence, 2013.##[34] G. Shaw, Y. Xu, and S. Geva, "Using association rules to solve the cold-start problem in recommender systems," in Pacific-Asia conference on knowledge discovery and data mining, 2010, pp. 340-347.##[35] Q. Liu, E. Chen, H. Xiong, C. H. Ding, and J. Chen, "Enhancing collaborative filtering by user interest expansion via personalized ranking," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 42, pp. 218-233, 2011.##[36] J. Demšar, "Statistical comparisons of classifiers over multiple data sets," Journal of Machine learning research, vol. 7, pp. 1-30, 2006.##[37] G. Karypis, "Evaluation of item-based top-n recommendation algorithms," in Proceedings of the tenth international conference on Information and knowledge management, 2001, pp. 247-254.##[38] P. Cremonesi, Y. Koren, and R. Turrin, "Performance of recommender algorithms on top-n recommendation tasks," in Proceedings of the fourth ACM conference on Recommender systems, 2010, pp. 39-46.##[39] R. Bambini, P. Cremonesi, and R. Turrin, "A recommender system for an IPTV service provider: a real large-scale production environment," in Recommender systems handbook, ed: Springer, 2011, pp. 299-331.##[40] H. Cui, M. Zhu, and S. Yao, "Ontology-based Top-N Recommendations on new items with matrix factorization," Journal of Software, vol. 9, pp. 2026-2032, 2014.##[41] J.Zhong, , H. Xie, & F.L. Wang, "The research trends in recommender systems for e-learning: A systematic review of SSCI journal articles from 2014 to 2018", Asian Association of Open Universities Journal, vol.14(1), pp.12-27, 2019.##[42] V.Vanitha, P. Krishnan, "A modified ant colony algorithm for personalized learning path construction", Journal of Intelligent & Fuzzy Systems, vol. 37(5), pp. 6785-6800.##[43] L. H. Son, Dealing with the new user cold-start problem in recommender systems: A comparative review. Information Systems, vol.58, pp. 87-104, 2016.##[44] N.Silva, D.Carvalho, A. C.Pereira, F. Mourão, & L.Rocha, "The pure cold-start problem: A deep study about how to conquer first-time users in recommendations domains", Information Systems, vol. 80, pp. 1-12, 2019.##[45] L.Romero, C. Saucedo, M. L. Caliusco, & M.Gutiérrez, "Supporting self-regulated learning and personalization using ePortfolios: a semantic approach based on learning paths", International Journal of Educational Technology in Higher Education, vol.16(1), pp.16, 2019.##[46] J.R. Almeida, E.Monteiro, L.B.Silva, A.P.Sierra, J.L.Oliveira, "A Recommender System to Help Discovering Cohorts in Rare Diseases", In Proceedings of the 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Rochester, MN, USA, pp.28-30, July, 2020. ##

روشی کارا بر پایه ترکیب مدل‌های یادگیری ژرف برای تجزیه ‌و تحلیل احساسات در متون Efficient Method Based on Combination of Deep Learning Models for Sentiment Analysis of Text 1 1 یکی از مهم‌ترین داده‌های متنی موجود در سطح وب احساسات و دید‌گاه‌‌های افراد نسبت به یک موضوع یا مفهوم مشخص است. با این حال، یافتن و نظارت بر وبگاه‌های حاوی این احساسات و استخراج اطلاعات موردنیاز از آن‌ها به‌علت گسترش وبگاه‌های گوناگون کاری دشوار محسوب می‌شود. در این راستا، توسعه سامانه‌های تجزیه ‌و تحلیل خودکار احساسات که بتواند نظرات را استخراج کرده و روند فکری مرتبط با آن‌ها را بیان کند، در سال‌های اخیر توجه زیادی را به خود جلب کرده است و روش‌های بر پایه یادگیری ژرف، یکی از راه‌کارهایی هستند که توانسته‌ا‌ند به نتایج چشم‌گیری در کاربردهای مختلف پردازش زبان‌های طبیعی به‌خصوص تجزیه ‌و تحلیل احساسات دست یابند؛ اما این روش‌ها برخلاف عملکرد قابل‌توجه هنوز با چالش‌هایی مواجه هستند و نیاز به پیشرفت در این حوزه همچنان وجود دارد؛ ازاین‌رو، هدف این مقاله ترکیب مدل‌های یادگیری ژرف به‌منظور ارائه یک روش جدید برای تجزیه ‌و تحلیل احساسات متنی است که بتواند ضمن استفاده هم‌زمان از مزایای شبکه‌های عصبی ژرف بر مشکلات آن‌ها چیره شود. در این راستا، در این مقاله روشی بر پایه ترکیب شبکه عصبی پیچشی و شبکه عصبی هم‌گشتی معرفی‌ شده است که در آن به‌منظور حفظ وابستگی‌های بلندمدت در جملات و کاهش از‌دست‌رفتن داده‌های محلی که به‌عنوان چالش‌های شبکه عصبی پیچشی به شمار‌ می‌آیند، از لایه هم‌گشتی تعمیم‌یافته که در آن از یک ویژگی میانی حاصل از ترکیب گره‌های فرزندان استفاده می‌شود، به‌عنوان جایگزین لایه ادغام در شبکه عصبی پیچشی بر پایه ساز‌و‌کار توجه استفاده شده است. بر اساس نتایج آزمایش‌ها، روش پیشنهادی به‌ترتیب با دقت 92/53 و 89/92 درصد روی مجموعه‌داده‌های SST1 و SST2  و دارای دقت بالاتری نسبت به سایر روش‌های موجود است.   2 People's opinions about a specific concept are considered as one of the most important textual data that are available on the web. However, finding and monitoring web pages containing these comments and extracting valuable information from them is very difficult. In this regard, developing automatic sentiment analysis systems that can extract opinions and express their intellectual process has attracted considerable attention in recent years. Sentiment analysis is considered as one of the most active research areas in the field of natural language processing which tries to classify a piece of text containing opinions based on its polarity and determine whether an expressed opinion about a specific topic, event or product is positive or negative. Since about a decade ago, many studies have been carried out to investigate the effects of traditional classification models, such as Support Vector Machine (SVM), Naïve Bayes, Logistic Regression, etc. in the task of sentiment analysis. Although machine learning models have achieved great success in this filed, they are still confronted with some limitations, notably manual feature engineering requirements. In other words, the classification performance of machine learning models is highly dependent on the extracted features and they play an important role in obtaining higher classification accuracy. To deal with these problems, deep learning models have been extensively employed as an alternative to traditional machine learning models and have achieved impressive results. It is worth mentioning that despite the remarkable performance of these methods, they are still confronted with some limitations and they are on their first steps of progress. Therefore, the goal of this paper is to propose a combinational deep learning model that can overcome their problems as well as utilizing their benefits. In this regard, an efficient method based on combination of convolutional and recursive neural networks is proposed in this paper that employs a generalized recursive neural network, where an intermediate feature is obtained by combining children's nodes, as an alternative of pooling layer in attention-based convolutional neural network with the aim of capturing long term dependencies and decreasing the loss of local information. Based on empirical results, the proposed method with the accuracy of 53.92% and 92.89% respectively on SST1 and SST2 datasets not only outperforms other existing models but also can be trained much faster. 19 38 2020/12/162019/08/15 1398/5/24 2021/05/222021/01/10 1399/10/21 حسین صدر Hossein Sadr مؤسسه آموزش عالی راهبرد شمال ایران Sadr@qiau.ac.ir میرمحسن پدرام Mir mohsen Pedram دانشگاه خوارزمی ایران Pedram@khu.ac.ir محمد تشنه لب Mohammad Teshnehlab دانشگاه صنعتی خواجه نصیر طوسی ایران Teshnehlab@eetd.kntu.ac.ir Sentiment analysis Deep Leaning Convolutional neural network Recursive neural network Attention mechanism تجزیه ‌و تحلیل احساسات یادگیری ژرف شبکه عصبی پیچشی شبکه عصبی هم‌گشتی ساز‌و‌کار توجه [1] H. Sadr, M. M. Pedram, and M. Teshnehlab, "A Robust Sentiment Analysis Method Based on Sequential Combination of Convolutional and Recursive Neural Networks," Neural Processing Letters, pp. 1-17, 2019.##[2] H. Sadr, M. M. Pedram, and M. Teshnelab, "Improving the Performance of Text Sentiment Analysis using Deep Convolutional Neural Network Integrated with Hierarchical Attention Layer," International Journal of Information and Communication Technology Research, vol. 11, no. 3, pp. 57-67, 2019.##[3] Mohades Deilami, Fatemeh, Hossein Sadr, and Morteza Tarkhan. "Contextualized Multidimensional Personality Recognition using Combination of Deep Neural Network and Ensemble Learning." Neural Processing Letters 2022: 1-18.##[4] V. Vyas and V. Uma, "Approaches to sentiment analysis on product reviews," in Sentiment Analysis and Knowledge Discovery in Contemporary Business: IGI Global, 2019, pp. 15-30.##[5] Kalashami, Mahsa Pourhosein, Mir Mohsen Pedram, and Hossein Sadr. "EEG Feature Extraction and Data Augmentation in Emotion Recognition." Computational Intelligence and Neuroscience 2022.##[6] S. M. H. Chowdhury, S. Abujar, M. Saifuzzaman, P. Ghosh, and S. A. Hossain, "Sentiment Prediction Based on Lexical Analysis Using Deep Learning," in Emerging Technologies in Data Mining and Information Security: Springer, 2019, pp. 441-449.##[7] Soleymanpour, Shiva, Hossein Sadr, and Mojdeh Nazari Soleimandarabi. "CSCNN: cost-sensitive convolutional neural network for encrypted traffic classification." Neural Processing Letters , pp.3497-3523, 2021.##[8] Sadr, Hossein, and Mojdeh Nazari Soleimandarabi. "ACNN-TL: attention-based convolutional neural network coupling with transfer learning and contextualized word representation for enhancing the performance of sentiment classification." The Journal of Supercomputing 2022, pp. 1-27, 2022.##[9] H. Sadr, M. Nazari, M. M. Pedram, and M. Teshnehlab, "Exploring the Efficiency of Topic-Based Models in Computing Semantic Relatedness of Geographic Terms," International Journal of Web Research, vol. 2, no. 2, pp. 23-35, 2019.##[10] H. Sadr, M. M. Pedram, and M. Teshnehlab, "Multi-View Deep Network: A Deep Model Based on Learning Features From Heterogeneous Neural Networks for Sentiment Analysis," IEEE Access, vol. 8, pp. 86984-86997, 2020.##[11] H. Sadr, M. N. Soleimandarabi, M. Pedram, and M. Teshnelab, "Unified Topic-Based Semantic Models: A Study in Computing the Semantic Relatedness of Geographic Terms," in 2019 5th International Conference on Web Research (ICWR), 2019: IEEE, pp. 134-140.##[12] V. D. Van, T. Thai, and M.-Q. Nghiem, "Combining convolution and recursive neural networks for sentiment analysis," in Proceedings of the Eighth International Symposium on Information and Communication Technology, 2017: ACM, pp. 151-158.##[13] N. C. Dang, M. N. Moreno-García, and F. De la Prieta, "Sentiment Analysis Based on Deep Learning: A Comparative Study," Electronics, vol. 9, no. 3, pp. 483, 2020.##[14] H. Sadr and M. Nazari Solimandarabi, "Presentation of an efficient automatic short answer grading model based on combination of pseudo relevance feedback and semantic relatedness measures," Journal of Advances in Computer Research, vol. 10, no. 2, pp. 1-10, 2019.##[15] J. Islam and Y. Zhang., "Visual Sentiment Analysis for Social Images Using Transfer Learning Approach," 2016 IEEE Int. Conf. Big Data Cloud Comput. (BDCloud), Soc. Comput. Netw. (SocialCom), Sustain. Comput. Commun., pp. 124130, 2016.##[16] X. Ouyang, P. Zhou, C. H. Li, and L. Liu, "Sentiment Analysis Using Convolutional Neural Network," Comput. Inf. Technol. Ubiquitous Comput. Commun. Dependable, Auton. Secur. Comput. Pervasive Intell. Comput. (CIT/IUCC/DASC/PICOM), 2015 IEEE Int. Conf., pp. 23592364, 2015.##[17] R. Yin, P. Li, and B. Wang, "Sentiment Lexical-Augmented Convolutional Neural Networks for Sentiment Analysis," IEEE Second International Conference on Data Science in Cyberspace, 2017.##[18] R. Socher, Pennington, E. H. Huang, A. Y. Ng, and C. D. Manning, "Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions," Proceedings of the Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics., 2011.##[19] R. Socher, B. Huval, C. D. Manning, and A. Y. Ng, "Semantic Compositionality through Recursive Matrix-Vector Spaces," Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Association for Computational Linguistics., 2012.##[20] R. Socher, A. Perelygin, and Wu, "Recursive deep models for semantic compositionality over a sentiment treebank," Proceedings of the conference on empirical methods in natural language processing (EMNLP), 2013.##[21] Q. Huang, X. Zheng, R. Chen, and Z. Dong, "Deep Sentiment Representation Based on CNN and LSTM " International Conference on Green Informatics, 2017.##[22] A. Hassan and A. Mahmood, "Deep Learning approach for sentiment analysis of short texts," in Control, Automation and Robotics (ICCAR), 2017 3rd International Conference on, 2.17 IEEE, pp. 705-710.##[23] A. Timmaraju and V. Khanna, "Sentiment Analysis on Movie Reviews using Recursive and Recurrent Neural Network Architectures," 2017.##[24] X. Wang, W. Jiang, and Z. Luo, "Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts," 2016.##[25] V. D. Van, Œ. Thai, and M.-Q. o. Nghiem, "Combining Convolution and Recursive Neural Networks for Sentiment Analysis," 2018.##[26] S. M. Rezaeinia, R. Rahmani, A. Ghodsi, and H. Veisi, "Sentiment analysis based on improved pre-trained word embeddings," Expert Systems with Applications, vol. 117, pp. 139-147, 2019.##[27] T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Distributed Representations of Words and Phrases and their Compositionality, Nips," 2013.##[28] O. Irsoy and C. Cardie, "Deep recursive neural networks for compositionality in language," in Advances in neural information processing systems, 2014, pp. 2096-2104.##[29] Y. Zhang and B. Wallace, "A sensitivity analysis of (and practitioners' guide to) convolutional neural networks for sentence classification," arXiv preprint arXiv:1510.03820, 2015.##[30] Y. LeCun, Y. Bengio, and G. Hinton, " Deep learning," Nature, vol. 521, no. 7553, pp. 436-444, May, 2015.##[31] C. DU and L. HUANG, "Sentiment Classification Via Recurrent Convolutional Neural Networks," DEStech Transactions on Computer Science and Engineering, no. cii, 2017.##[32] N. Kalchbrenner, E. Grefenstette, and P. Blunsom, "A convolutional neural network for modelling sentences," arXiv preprint arXiv:1404.2188, 2014.##[33] Y. Kim, "Convolutional neural networks for sentence classification," arXiv preprint arXiv:1408.5882, 2014.##[34] W. Yin and H. Schütze, "Multichannel variable-size convolution for sentence classification," arXiv preprint arXiv:1603.04513, 2016.##[35] K. S. Tai, R. Socher, and C. D. Manning, "Improved semantic representations from tree-structured long short-term memory networks," arXiv preprint arXiv:1503.00075, 2015.##[36] F. Kokkinos and A. Potamianos, "Structural attention neural networks for improved sentiment analysis," arXiv preprint arXiv:1701.01811, 2017.##[37] Y. Wang, M. Huang, and L. Zhao, "Attention-based LSTM for aspect-level sentiment classification," in Proceedings of the 2016 conference on empirical methods in natural language processing, 2016, pp. 606-615.##[38] Sadr, Hossein, Mir M. Pedram, and Mohammad Teshnehlab. "Convolutional neural network equipped with attention mechanism and transfer learning for enhancing performance of sentiment analysis." Journal of AI and Data Mining 9.2, 2021 : 141-151.##[1] H. Sadr, M. M. Pedram, and M. Teshnehlab, "A Robust Sentiment Analysis Method Based on Sequential Combination of Convolutional and Recursive Neural Networks," Neural Processing Letters, pp. 1-17, 2019.##[2] H. Sadr, M. M. Pedram, and M. Teshnelab, "Improving the Performance of Text Sentiment Analysis using Deep Convolutional Neural Network Integrated with Hierarchical Attention Layer," International Journal of Information and Communication Technology Research, vol. 11, no. 3, pp. 57-67, 2019.##[3] Mohades Deilami, Fatemeh, Hossein Sadr, and Morteza Tarkhan. "Contextualized Multidimensional Personality Recognition using Combination of Deep Neural Network and Ensemble Learning." Neural Processing Letters 2022: 1-18.##[4] V. Vyas and V. Uma, "Approaches to sentiment analysis on product reviews," in Sentiment Analysis and Knowledge Discovery in Contemporary Business: IGI Global, 2019, pp. 15-30.##[5] Kalashami, Mahsa Pourhosein, Mir Mohsen Pedram, and Hossein Sadr. "EEG Feature Extraction and Data Augmentation in Emotion Recognition." Computational Intelligence and Neuroscience 2022.##[6] S. M. H. Chowdhury, S. Abujar, M. Saifuzzaman, P. Ghosh, and S. A. Hossain, "Sentiment Prediction Based on Lexical Analysis Using Deep Learning," in Emerging Technologies in Data Mining and Information Security: Springer, 2019, pp. 441-449.##[7] Soleymanpour, Shiva, Hossein Sadr, and Mojdeh Nazari Soleimandarabi. "CSCNN: cost-sensitive convolutional neural network for encrypted traffic classification." Neural Processing Letters , pp.3497-3523, 2021.##[8] Sadr, Hossein, and Mojdeh Nazari Soleimandarabi. "ACNN-TL: attention-based convolutional neural network coupling with transfer learning and contextualized word representation for enhancing the performance of sentiment classification." The Journal of Supercomputing 2022, pp. 1-27, 2022.##[9] H. Sadr, M. Nazari, M. M. Pedram, and M. Teshnehlab, "Exploring the Efficiency of Topic-Based Models in Computing Semantic Relatedness of Geographic Terms," International Journal of Web Research, vol. 2, no. 2, pp. 23-35, 2019.##[10] H. Sadr, M. M. Pedram, and M. Teshnehlab, "Multi-View Deep Network: A Deep Model Based on Learning Features From Heterogeneous Neural Networks for Sentiment Analysis," IEEE Access, vol. 8, pp. 86984-86997, 2020.##[11] H. Sadr, M. N. Soleimandarabi, M. Pedram, and M. Teshnelab, "Unified Topic-Based Semantic Models: A Study in Computing the Semantic Relatedness of Geographic Terms," in 2019 5th International Conference on Web Research (ICWR), 2019: IEEE, pp. 134-140.##[12] V. D. Van, T. Thai, and M.-Q. Nghiem, "Combining convolution and recursive neural networks for sentiment analysis," in Proceedings of the Eighth International Symposium on Information and Communication Technology, 2017: ACM, pp. 151-158.##[13] N. C. Dang, M. N. Moreno-García, and F. De la Prieta, "Sentiment Analysis Based on Deep Learning: A Comparative Study," Electronics, vol. 9, no. 3, pp. 483, 2020.##[14] H. Sadr and M. Nazari Solimandarabi, "Presentation of an efficient automatic short answer grading model based on combination of pseudo relevance feedback and semantic relatedness measures," Journal of Advances in Computer Research, vol. 10, no. 2, pp. 1-10, 2019.##[15] J. Islam and Y. Zhang., "Visual Sentiment Analysis for Social Images Using Transfer Learning Approach," 2016 IEEE Int. Conf. Big Data Cloud Comput. (BDCloud), Soc. Comput. Netw. (SocialCom), Sustain. Comput. Commun., pp. 124130, 2016.##[16] X. Ouyang, P. Zhou, C. H. Li, and L. Liu, "Sentiment Analysis Using Convolutional Neural Network," Comput. Inf. Technol. Ubiquitous Comput. Commun. Dependable, Auton. Secur. Comput. Pervasive Intell. Comput. (CIT/IUCC/DASC/PICOM), 2015 IEEE Int. Conf., pp. 23592364, 2015.##[17] R. Yin, P. Li, and B. Wang, "Sentiment Lexical-Augmented Convolutional Neural Networks for Sentiment Analysis," IEEE Second International Conference on Data Science in Cyberspace, 2017.##[18] R. Socher, Pennington, E. H. Huang, A. Y. Ng, and C. D. Manning, "Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions," Proceedings of the Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics., 2011.##[19] R. Socher, B. Huval, C. D. Manning, and A. Y. Ng, "Semantic Compositionality through Recursive Matrix-Vector Spaces," Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Association for Computational Linguistics., 2012.##[20] R. Socher, A. Perelygin, and Wu, "Recursive deep models for semantic compositionality over a sentiment treebank," Proceedings of the conference on empirical methods in natural language processing (EMNLP), 2013.##[21] Q. Huang, X. Zheng, R. Chen, and Z. Dong, "Deep Sentiment Representation Based on CNN and LSTM " International Conference on Green Informatics, 2017.##[22] A. Hassan and A. Mahmood, "Deep Learning approach for sentiment analysis of short texts," in Control, Automation and Robotics (ICCAR), 2017 3rd International Conference on, 2.17 IEEE, pp. 705-710.##[23] A. Timmaraju and V. Khanna, "Sentiment Analysis on Movie Reviews using Recursive and Recurrent Neural Network Architectures," 2017.##[24] X. Wang, W. Jiang, and Z. Luo, "Combination of Convolutional and Recurrent Neural Network for Sentiment Analysis of Short Texts," 2016.##[25] V. D. Van, Œ. Thai, and M.-Q. o. Nghiem, "Combining Convolution and Recursive Neural Networks for Sentiment Analysis," 2018.##[26] S. M. Rezaeinia, R. Rahmani, A. Ghodsi, and H. Veisi, "Sentiment analysis based on improved pre-trained word embeddings," Expert Systems with Applications, vol. 117, pp. 139-147, 2019.##[27] T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Distributed Representations of Words and Phrases and their Compositionality, Nips," 2013.##[28] O. Irsoy and C. Cardie, "Deep recursive neural networks for compositionality in language," in Advances in neural information processing systems, 2014, pp. 2096-2104.##[29] Y. Zhang and B. Wallace, "A sensitivity analysis of (and practitioners' guide to) convolutional neural networks for sentence classification," arXiv preprint arXiv:1510.03820, 2015.##[30] Y. LeCun, Y. Bengio, and G. Hinton, " Deep learning," Nature, vol. 521, no. 7553, pp. 436-444, May, 2015.##[31] C. DU and L. HUANG, "Sentiment Classification Via Recurrent Convolutional Neural Networks," DEStech Transactions on Computer Science and Engineering, no. cii, 2017.##[32] N. Kalchbrenner, E. Grefenstette, and P. Blunsom, "A convolutional neural network for modelling sentences," arXiv preprint arXiv:1404.2188, 2014.##[33] Y. Kim, "Convolutional neural networks for sentence classification," arXiv preprint arXiv:1408.5882, 2014.##[34] W. Yin and H. Schütze, "Multichannel variable-size convolution for sentence classification," arXiv preprint arXiv:1603.04513, 2016.##[35] K. S. Tai, R. Socher, and C. D. Manning, "Improved semantic representations from tree-structured long short-term memory networks," arXiv preprint arXiv:1503.00075, 2015.##[36] F. Kokkinos and A. Potamianos, "Structural attention neural networks for improved sentiment analysis," arXiv preprint arXiv:1701.01811, 2017.##[37] Y. Wang, M. Huang, and L. Zhao, "Attention-based LSTM for aspect-level sentiment classification," in Proceedings of the 2016 conference on empirical methods in natural language processing, 2016, pp. 606-615.##[38] Sadr, Hossein, Mir M. Pedram, and Mohammad Teshnehlab. "Convolutional neural network equipped with attention mechanism and transfer learning for enhancing performance of sentiment analysis." Journal of AI and Data Mining 9.2, 2021 : 141-151. ##

ارائه مدل یادگیر ترکیب کرنل‌ها برای پیش‌بینی سری‌های زمانی براساس رگرسیون بردار پشتیبان و جستجوی فراابتکاری Ensemble Kernel Learning Model for Prediction of Time Series Based on the Support Vector Regression and Meta Heuristic Search 1 1 در این مقاله به ارائه روشی برای پیش­بینی سری­زمانی پرداخته شده است. مدلی که در این مقاله ارائه شده بر پایه ترکیب کرنل‌ها و رگرسیون بردار پشتیبان است. رگرسیون بردار پشتیبان با استفاده از کرنل هایش توانایی بالایی در حل مسائل تخمین توابع دارد؛ اما این کرنل­ها پارامترهایی دارند که نیاز به تنظیم دارند. در مدل پیشنهادی کرنل­های مختلف بر روی داده­ها اعمال می­شوند. خروجی کرنل­ها با اعمال یک ضریب، با هم ترکیب می­شوند. این ترکیب باعث می­شود یک فضای ثانویه جدیدی به‌دست آید. دلیل این امر این است که، ممکن است از بین کرنل­های موجود فقط یک تعدادی از آن­ها با ضریب خاصی برای صورت مسأله مفید باشد و ما از این‌که کدام کرنل برای صورت مسأله ما کارا است آگاه نیستیم. همچنین هرکدام از کرنل­ها پارامتر­هایی دارند که باید مقادیر بهینه آن­ها برای دست‌یابی به نتیجه بهتر تعیین شوند؛ از‌این‌رو در مدل ارائه‌شده، یادگیری پارامتر­های کرنل و وزن­های آن­ها توسط بهینه‌ساز گرگ خاکستری انجام می­شود مدل پیشنهادی  روی پنج مجموعه سری­­زمانی استاندارد پیاده­سازی شده­ که نتایج تست براساس معیار RMSE برای سری­زمانی DJ، 58/1، سری­زمانیRadio ، 178/0، سری زمانی Sunspot ، 709/1، نسبت به روش­های دیگر بهتر شده­ است.  همچنین در انتها به تحلیل نتایج، ارزیابی آماری با آزمون ویلکاکسون رتبه علامت­دار و ارائه رابطه برای یافتن اندازه پنجره در مدل پرداخته شده است. 2 In this paper, a method is presented for predicting time series. Time series prediction is a process which predicted future system values based on information obtained from past and present data points. Time series prediction models are widely used in various fields of engineering, economics, etc. The main purpose of using different models for time series prediction is to make the forecast with the greatest accuracy. The model presented in this paper is based on the combination of kernels and support vector regression. Support vector regression is highly capable of solving function estimation problems by using its kernels, but kernels’ parameters need to be adjusted. First we have preprocessing phase which includes normalizing data and separating data for testing and training. In proposed model, ten different kernels were used. Five kernels were selected as the best kernels by trial and error and these kernels are applied to data. There probably is only a few of the kernels that are useful for the problem, and we are not aware of which kernels are useful for our problem so kernel outputs aggregate by applying a coefficient. This combination creates a new secondary space. The output is given to support vector regression to construct a model that predicts values exactly ɛ accurate, which means the predicted values do not deviate more than ɛ from the original data. This model predicts values by using a leave one out model. Each kernel has parameters that need to be set to optimum values in order to get the best results. Hence in the proposed model, the kernel parameters and their weights are learned by the Gray Wolf Optimizer. This optimizer has been able to provide appropriate answers to many problems, especially challenging problems and has a superior ability to solve the high-dimension problems. By running program in consecutive iterations and examining the different values of the parameters, the optimizer learns the best of them which prediction error has been reduced, and finally returns their best value. The proposed model is implemented on five standard time series and compared to other method, test based on the RMSE criterion for DJ time series, improved by 1.58 point, Radio time series, improved by 0.178 point, and Sunspot time series, improved by 1.709 point. Finally, we analyzed the results, Statistical evaluation by Wilcoxon Signed-Rank Test where the p value is very low compared to the proposed method and CNN-FCM, AR_ model per scale, Multiresolution AR model and ANN methods, slightly lower for Wavelet-HFCM and ANFIS methods and slightly lower than one for SAE-FCM method and at the end provide a relation to find the window size in the model by obtaining the average of peak differences, valley differences, and consecutive peak, and valley differences for the actual values of the training data in exchange for their sequence number in time series. 39 42 2020/12/162019/08/152020/08/9 1399/5/19 2021/05/222021/01/102021/01/10 1399/10/21 حدیثه پورعلی Hadiseh Pourali دانشگاه صنعتی نوشیروانی بابل ایران h.poorali@nit.ac.ir حسام عمرانپور Hesam Omranpour دانشگاه صنعتی نوشیروانی ایران h.omranpour@nit.ac.ir Time series prediction Support vector regression Ensemble kernel model Optimization پیش‌بینی سری‌زمانی رگرسیون بردار پشتیبان ترکیب توابع کرنل بهینه‌سازی [1] E. Kayacan, B. Ulutas and O. Kaynak, "Grey system theory-based models in time series prediction," Expert Systems with Applications, vol. 37, no. 2, pp. 1784-1789, 2010.##[2] B. Paaben, C. Gopfert and B. Hammer, "Time Series Prediction for Graph in Kernel andDissimilarity Spaces," Neural Processing Letters, no. 48, pp. 669-689, 2018.##[3] S. C. Nayak, B. B. Misra and H. S. Behera, "Efficient financial time series prediction with evolutionary virtual data position exploration," Neural Computing and Application, no. 31, pp. 1053-1074, 2019.##[4] M. A. Villegas, D. J. Pedregal and J. R. Trapero, "A support vector machine for model selection in demand forecasting application," Computers & Industrial Engineering, vol. 121, pp. 1-7, 2018.##[5] P. Liu, J. Liu and K. Wu, "CNN-FCM: System modeling promotes stability of deep learning in time series prediction," Knowledge-Based Systems, vol. 203, 2020.##[6] J. H. Sadaei, P. Cândido de Lima e Silva, F. Gadelha Guimarães and M. Hisyam Lee, "Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series," Energy, vol. 175, pp. 365-377, 2019.##[7] J. Hu, X. Wang, Y. Zhang, D. Zhang, M. Zhang and J. Xue, "Time Series Prediction Method Based on Variant LSTM Recurrent Neural Network," Neural Processing Letters, 2020.##[8] K. Yuan, J. Liu, S. Yang, K. Wu and F. Shen, "Time series forecasting based on kernel mapping and high-order fuzzy cognitive maps," Knowledge-Based Systems, vol. 206, 2020.##[9] J. Wang, Z. Peng, X. Wang, C. Li and J. Wu, "Deep Fuzzy Cognitive Maps for Interpretable Multivariate Time Series Prediction," IEEE Transactions on Fuzzy Systems, pp. 1-1, 2020.##[10] S. Yang and J. Liu, "Time Series Forecasting based on High-Order Fuzzy Cognitive Maps and Wavelet Transform," IEEE Transactions on Fuzzy System, vol. 26, no. 6, pp. 3391-3402, 2018.##[11] Q. Xiao, "Time series prediction using bayesian filtering model and fuzzy neural networks," Optik - International Journal for Light and Electron Optics, vol. 140, pp. 104-113, 2017.##[12] H. Omranpour, F. Azadian, "Presenting a Fuzzy Approach to Optimize Predicting High Order Time series," Signal and Data Processing vol. 15, no. 2, pp. 3-16, 2018.##[13] C. Bergmeir, R. J. Hyndman and B. Koo, "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics and Data Analysis, vol. 120, pp. 70-83, 2018.##[14] W. Xu, H. Peng, X. Zeng, F. Zhou, X. Tian and X. Peng, "Deep belief network-based AR model for nonlinear time series forecasting," Applied Soft Computing Journal, vol. 77, pp. 605-621, 2019.##[15] L. Bianchi, M. Dorigo, L. M. Gambardella and W. J. Gutjahr, "A survey on metaheuristics for stochastic combinatorial optimization," Natural Computing, vol. 8, pp. 239-287, 2009.##[16] G. Cornuéjols , "Valid inequalities for mixed integer linear programs," Mathematical Programming, vol. 112, pp. 3-44, 2008.##[17] M. Avriel, Nonlinear Programming: Analysis and Methods, New York: Dover Publications, 2003.##[18] A. H. Land and A. G. Doig, "An automatic method for solving discrete programming problems," 50 Years of Integer Programming 1958-2008, pp. 105-132, 2010.##[19] A. R. Simpson, G. C. Dancy and L. J. Murphy, "Genetic algorithms compared to other techniques for pipe optimization," Journal of water resources planning and management, vol. 120, no. 4, pp. 423-443, 1994.##[20] S. Mirjalili, "The Ant Lion Optimizer," Advances in Engineering Software, vol. 83, pp. 80-98, 2015.##[21] B. T. Ojemakinde, Support Vector Regression for Non-Stationary Time Series, Knoxville: University of Tennessee, 2006.##[22] S. Lin, S. zhang, J. Qiao, H. Liu and G. Yu, "A Parameter Choosing Method of SVR for Time Series Prediction," in The 9th International Conference for Young Computer Scientists, Liaoning, China, 2008.##[23] C. Hsin, J. M. Ho and D. T. Lee, "Travel-Time Prediction With Support Vector Regression," IEEE Transactions on Intelligent Transportation Systems, vol. 5, no. 4, pp. 276-281, 2004.##[24] X. Ma, Y. Zhang, H. Cao, S. Zhang and Y. Zhou, "Nonlinear Regression with High-Dimensional Space Mapping for Blood Component Spectral Quantitative Analysis," Journal of Spectroscopy, 2018.##[25] T. Hofmann, B. Schölkopf and A. J. Smola, "Kernel Methods in Machine Learning," Institute of Mathematical Statistics, vol. 36, no. 3, pp. 1171-1220, 2008.##[26] J. Xie, "Time Series Prediction Based on Recurrent LS-SVM with Mixed Kernel," in Asia-Pacific Conference on Information Processing, Shenzhen, China, 2009.##[27] S. Mirjalili, S. M. Mirjalili and A. Lewis, "Grey Wolf Optimizer," Advances in Engineering Software, vol. 69, pp. 46-61, 2014.##[28] M. R. Mosavi, M. Khishe and A. Ghamgosar, "CLASSIFICATION OF SONAR DATA SET USING NEURAL NETWORK TRAINED BY GRAY WOLF OPTIMIZATION," Neural Network World, vol. 4, pp. 393-415, 2016.##[29] J. Heinermann and O. Kramer, "Precise Wind Power Prediction with SVM Ensemble Regression," in International Conference on Artificial Neural Networks, Hamburg, Germany, 2014.##[30] K. Wu, J. Liu, P. Liu and S. Yang, "Time Series Prediction Using Sparse Autoencoder and High-order Fuzzy Cognitive Maps," IEEE Transactions on Fuzzy Systems, 2019.##[31] J. Shing and R. Jang, "ANFIS: adaptive-network-based fuzzy inference system," IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 665-685, 1993.##[32] G. Zheng, J. Starck, J. Campbell and F. Murtagh, "Multiscale transforms for filtering financial data streams," Journal of Computational Intelligence in Finance, vol. 7, no. 18-35, 1999.##[33] O. Renaud, J. L. Starck and F. Murtagh, "Wavelet-Based Combined Signal Filtering and Prediction," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 35, no. 6, pp. 1241-1251, 2005.##[34] A. B. Geva, "ScaleNet-multiscale neural-network architecture for time series prediction," IEEE Transactions on Neural Networks, vol. 9, no. 6, pp. 1471-1482, 1998.##[35] J. Derrac, S. García, D. Molina and F. Herrera, "A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms," Swarm and Evolutionary Computation, vol. 1, no. 1, pp. 3-18, 2011.##[1] E. Kayacan, B. Ulutas and O. Kaynak, "Grey system theory-based models in time series prediction," Expert Systems with Applications, vol. 37, no. 2, pp. 1784-1789, 2010.##[2] B. Paaben, C. Gopfert and B. Hammer, "Time Series Prediction for Graph in Kernel andDissimilarity Spaces," Neural Processing Letters, no. 48, pp. 669-689, 2018.##[3] S. C. Nayak, B. B. Misra and H. S. Behera, "Efficient financial time series prediction with evolutionary virtual data position exploration," Neural Computing and Application, no. 31, pp. 1053-1074, 2019.##[4] M. A. Villegas, D. J. Pedregal and J. R. Trapero, "A support vector machine for model selection in demand forecasting application," Computers & Industrial Engineering, vol. 121, pp. 1-7, 2018.##[5] P. Liu, J. Liu and K. Wu, "CNN-FCM: System modeling promotes stability of deep learning in time series prediction," Knowledge-Based Systems, vol. 203, 2020.##[6] J. H. Sadaei, P. Cândido de Lima e Silva, F. Gadelha Guimarães and M. Hisyam Lee, "Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series," Energy, vol. 175, pp. 365-377, 2019.##[7] J. Hu, X. Wang, Y. Zhang, D. Zhang, M. Zhang and J. Xue, "Time Series Prediction Method Based on Variant LSTM Recurrent Neural Network," Neural Processing Letters, 2020.##[8] K. Yuan, J. Liu, S. Yang, K. Wu and F. Shen, "Time series forecasting based on kernel mapping and high-order fuzzy cognitive maps," Knowledge-Based Systems, vol. 206, 2020.##[9] J. Wang, Z. Peng, X. Wang, C. Li and J. Wu, "Deep Fuzzy Cognitive Maps for Interpretable Multivariate Time Series Prediction," IEEE Transactions on Fuzzy Systems, pp. 1-1, 2020.##[10] S. Yang and J. Liu, "Time Series Forecasting based on High-Order Fuzzy Cognitive Maps and Wavelet Transform," IEEE Transactions on Fuzzy System, vol. 26, no. 6, pp. 3391-3402, 2018.##[11] Q. Xiao, "Time series prediction using bayesian filtering model and fuzzy neural networks," Optik - International Journal for Light and Electron Optics, vol. 140, pp. 104-113, 2017.##[12] H. Omranpour, F. Azadian, "Presenting a Fuzzy Approach to Optimize Predicting High Order Time series," Signal and Data Processing vol. 15, no. 2, pp. 3-16, 2018.##[12] ح. عمرانپور, ف. آزادیان, "ارائه یک رویکرد فازی برای بهینه سازی پیش بینی سری زمانی با مرتبه ی بالا," پردازش علائم و داده‌ها, جلد 15, شماره 2, 1397.##[13] C. Bergmeir, R. J. Hyndman and B. Koo, "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics and Data Analysis, vol. 120, pp. 70-83, 2018.##[14] W. Xu, H. Peng, X. Zeng, F. Zhou, X. Tian and X. Peng, "Deep belief network-based AR model for nonlinear time series forecasting," Applied Soft Computing Journal, vol. 77, pp. 605-621, 2019.##[15] L. Bianchi, M. Dorigo, L. M. Gambardella and W. J. Gutjahr, "A survey on metaheuristics for stochastic combinatorial optimization," Natural Computing, vol. 8, pp. 239-287, 2009.##[16] G. Cornuéjols , "Valid inequalities for mixed integer linear programs," Mathematical Programming, vol. 112, pp. 3-44, 2008.##[17] M. Avriel, Nonlinear Programming: Analysis and Methods, New York: Dover Publications, 2003.##[18] A. H. Land and A. G. Doig, "An automatic method for solving discrete programming problems," 50 Years of Integer Programming 1958-2008, pp. 105-132, 2010.##[19] A. R. Simpson, G. C. Dancy and L. J. Murphy, "Genetic algorithms compared to other techniques for pipe optimization," Journal of water resources planning and management, vol. 120, no. 4, pp. 423-443, 1994.##[20] S. Mirjalili, "The Ant Lion Optimizer," Advances in Engineering Software, vol. 83, pp. 80-98, 2015.##[21] B. T. Ojemakinde, Support Vector Regression for Non-Stationary Time Series, Knoxville: University of Tennessee, 2006.##[22] S. Lin, S. zhang, J. Qiao, H. Liu and G. Yu, "A Parameter Choosing Method of SVR for Time Series Prediction," in The 9th International Conference for Young Computer Scientists, Liaoning, China, 2008.##[23] C. Hsin, J. M. Ho and D. T. Lee, "Travel-Time Prediction With Support Vector Regression," IEEE Transactions on Intelligent Transportation Systems, vol. 5, no. 4, pp. 276-281, 2004.##[24] X. Ma, Y. Zhang, H. Cao, S. Zhang and Y. Zhou, "Nonlinear Regression with High-Dimensional Space Mapping for Blood Component Spectral Quantitative Analysis," Journal of Spectroscopy, 2018.##[25] T. Hofmann, B. Schölkopf and A. J. Smola, "Kernel Methods in Machine Learning," Institute of Mathematical Statistics, vol. 36, no. 3, pp. 1171-1220, 2008.##[26] J. Xie, "Time Series Prediction Based on Recurrent LS-SVM with Mixed Kernel," in Asia-Pacific Conference on Information Processing, Shenzhen, China, 2009.##[27] S. Mirjalili, S. M. Mirjalili and A. Lewis, "Grey Wolf Optimizer," Advances in Engineering Software, vol. 69, pp. 46-61, 2014.##[28] M. R. Mosavi, M. Khishe and A. Ghamgosar, "CLASSIFICATION OF SONAR DATA SET USING NEURAL NETWORK TRAINED BY GRAY WOLF OPTIMIZATION," Neural Network World, vol. 4, pp. 393-415, 2016.##[29] J. Heinermann and O. Kramer, "Precise Wind Power Prediction with SVM Ensemble Regression," in International Conference on Artificial Neural Networks, Hamburg, Germany, 2014.##[30] K. Wu, J. Liu, P. Liu and S. Yang, "Time Series Prediction Using Sparse Autoencoder and High-order Fuzzy Cognitive Maps," IEEE Transactions on Fuzzy Systems, 2019.##[31] J. Shing and R. Jang, "ANFIS: adaptive-network-based fuzzy inference system," IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 665-685, 1993.##[32] G. Zheng, J. Starck, J. Campbell and F. Murtagh, "Multiscale transforms for filtering financial data streams," Journal of Computational Intelligence in Finance, vol. 7, no. 18-35, 1999.##[33] O. Renaud, J. L. Starck and F. Murtagh, "Wavelet-Based Combined Signal Filtering and Prediction," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 35, no. 6, pp. 1241-1251, 2005.##[34] A. B. Geva, "ScaleNet-multiscale neural-network architecture for time series prediction," IEEE Transactions on Neural Networks, vol. 9, no. 6, pp. 1471-1482, 1998.##[35] J. Derrac, S. García, D. Molina and F. Herrera, "A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms," Swarm and Evolutionary Computation, vol. 1, no. 1, pp. 3-18, 2011. ##

طراحی یک سازوکار ارتباطی آگاه از ازدحام برای شبکه بی‌سیم روی تراشه در سیستم‌های چندهسته‌ای Design of a novel congestion-aware communication mechanism for wireless NoC in multicore systems 1 1 معماری ترکیبی بی­سیم شبکه روی تراشه به‌عنوان یک زیرساخت ارتباطی جدید جهت غلبه بر مشکلات معماری شبکه روی تراشه سنتی در سامانه‌های چندهسته‌ای پیشنهاد شده است. این معماری می‌تواند ارتباطاتی با پهنای باند بالا و توان مصرفی پایین برای سامانه‌های چند پردازنده­ای روی تراشه را فراهم کند. از آنجا که هر مسیریاب بی‌سیم بین مجموعه­ای از هسته­های پردازشی مشترک است، احتمال ازدحام مسیریاب­ها بالا می­رود و در‌نتیجه منجر به افزایش تأخیر ارسال و مصرف توان می­شود. در این مقاله یک معماری ترکیبی بی­سیم روی تراشه شامل توپولوژی و ساز و کار آگاه از ازدحام ارتباطی با توجه به بهینه‌سازی کارایی و هزینه سامانه ارائه می‌شود. با استفاده از شبیه‌سازی، کارایی معماری پیشنهادی در مقایسه با معماری­های مهم بی‌سیم روی تراشه مورد ارزیابی قرار می‌گیرد. نتایج شبیه‌سازی، مؤثر‌بودن معماری پیشنهادی را از منظر بهره‌وری شبکه، تأخیر ارسالی و مصرف توان تحت الگوهای ترافیکی گوناگون نشان می­دهد. 2 Network-on-Chip (NoC) has emerged as leading interconnection backbone to integrate numerous blocks in a single chip. Although it offers a high-performance communication infrastructure by using integrated switch-based networks, the possible performance improvement of a conventional NoC is restricted by multi-hop communications due to high transmission latency and power consumption incurred by the data transmission between two distant cores.  In order to mitigate this problem, wireless NoC (WNoC) architecture has proposed as an alternative solution to design flexible, low-power, and high bandwidth communication infrastructures for the future multicore platforms. It is necessary to mention that wire-based interconnections are still highly effective for short distances communications. Therefore, hybrid WNoC architectures are emerged as scalable communication structure to alleviate the deficits of traditional NOC architecture for the modern multicore systems. The hybrid WNoC architecture provides energy efficient, high data rate and flexible communications for NoC architectures. In these architectures, each wireless router is shared by a set of processing cores. However, sharing links between cores increases congestion in the network that limits the performance and scalability of NoCs and affects the system to work at less than its peak gain. Moreover, the congestion can heightens network inefficiency when the network is scaled to more nodes. In this paper, we propose a novel congestion-aware mesh-based WNoC architecture to address these issues. We consider optimization of the system cost and performance, simultaneously. For congestion control, it is recommended to include a multi-path routing. This means that several routes are calculated and recorded for each destination and finally the traffic load is distributed. Paths are selected based on their scores, which are obtained dynamically. When a path is used to transmit packets, the score of that path is reduced so that fewer packets are sent from that path and more scored paths are used. This approach aims to the distribution of traffic loads on the paths. The performance of the proposed architecture has been evaluated and compared with notable WNoC architectures through comprehensive simulations. The experimental results demonstrated the effectiveness of the proposed design under both synthetic and realistic traffic patterns in terms of network throughput, latency, and energy consumption. 43 58 2020/12/162019/08/152020/08/92019/09/28 1398/7/6 2021/05/222021/01/102021/01/102020/08/18 1399/5/28 عباس دهقانی Abbas Dehghani دانشگاه یاسوج ایران Dehghani.abas@gmail.com کیوان رحیمی زاده Keyvan RahimiZadeh دانشگاه یاسوج ایران RahimiZadeh@gmail.com Network on Chip Wireless communications Multicore System-on-Chip Congestion شبکه روی تراشه اتصالات بی‌سیم چند‌پردازنده‌ای روی تراشه چند‌هسته‌ای ازدحام [1] "2013 ITRS Edition." [Online]. Available: http://www.itrs.net/Links/2013ITRS/Home2013.htm. [Accessed: 02-Mar-2019].##[2] A. Rezaei, M. Daneshtalab, F. Safaei, and D. 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نهان‌نگاری در رایانامه با ظرفیت نامحدود از طریق لغت‌نامه A High Capacity Email Steganography Scheme using Dictionary 1 1 هدف اصلی در نهان‍نگاری پنهان‍سازی یک پیام مخفی با قراردادن آن پیام در یک رسانه پوشانه است؛ به‌نحوی که کمینه تغییرات در رسانه ایجاد شده و آن تغییرات به‌راحتی قابل درک نباشد. رسانه پوشانه می‍تواند یک بستر قابل دسترس توسط عموم نظیر متن، رایانامه، صوت، تصویر یا ویدئو باشد. با گسترش استفاده از رایانامه در بین کاربران اینترنتی، ارائه روش‍های نهان‍گاری در بستر رایانامه مورد توجه قرار گرفته است؛ ولی روش‍های موجود دارای محدودیت در ظرفیت نهان‍نگاری بوده و به‌طور‌عمومی مصالحه‍ای بین امنیت و ظرفیت نهان‍نگاری در نظر می‍گیرند. در این مقاله یک روش نوین برای نهان‌نگاری رایانامه ارائه شده است که مبتنی بر لغت‌نامه بوده و هم‌زمان ظرفیت نامحدود و امنیت بالایی را ارائه می‌کند. در گام نخست روش پیشنهادی، پیام به‌وسیله یک لغت‌نامه فشرده و رمز‌شده و سپس به یک رشته‌بیتی تبدیل می­شود. در هر مرحله با توجه به تعداد نویسه‌های محتوای رایانامه، قسمتی از رشته انتخاب‌شده، معادل ده‌دهی آن محاسبه شده و سپس با توجه به کلیدهای موجود، با آن‌ها نشانی‌های رایانامه ساخته می‍شود. ظرفیت نهان‌نگاری نامحدود در روش پیشنهادی منجر به امکان مخفی‌سازی هر میزان پیام در متن پوشانه شده است. همچنین نتایج آزمایش‌ها نشان می‌دهد که استفاده از لغت‌نامه منجر به کاهش حجم پیام و همچنین کاهش تعداد نشانی‌های گیرنده به میزان حدودی 44 درصد در مقایسه با روش‌های موجود شده است. این مهم به‌طور مستقیم به افزایش سطح امنیت روش پیشنهادی کمک می‌کند. 2 The expansion of the use of information exchange space and public access to communication networks such as the Internet has led to the growing dependence of social institutions on the use of these networks. However, maintaining the security of information exchanged on networks is one of the most important challenges for users of these networks. One way to protect this data is to use private networks. But building these networks is not cost-effective in terms of time and cost. In contrast, the use of encryption techniques, access control mechanisms and data concealment are among the effective solutions for security in the information exchange space. Existing methods for hiding information can be divided into three categories: cryptography, watermarking and steganography. In cryptography, a simple text is converted into encrypted text, which, of course, requires a decryption operation as well as an encryption key. In general, cryptographic techniques suffer from two major problems. The first problem is the ban on the transmission of encrypted data in dictatorial regimes, and the second problem is that cryptographers pay attention to encrypted data and stop any secret communication. The second category of information hiding methods is watermarking. Watermarking techniques are commonly used to protect the copyright of a digital content and to deal with issues such as fraud, fraud and copyright infringement in the data transfer space. In steganography methods, the transfer of information takes place in a cover through public communication channels, and only the sender and receiver are aware of the existence of a secret message. Two aspects of steganography must be observed. The first aspect is that the cover and secret content look the same in the face of statistical attacks. The second aspect is that the process of hiding the secret message in the cover is such that there is no difference between the cover and the secret in terms of the human perceptual system. In fact, the accuracy of the transmission media is maintained. Steganography methods use image, video, protocol, audio, and text platforms to hide information. Steganography in the text is difficult due to very little local variation. Humans are very sensitive to textual changes. Hence it is difficult to spell in the text. However, due to the high use of text in digital media, the insensitivity of text to compression, the need for less memory to store and communicate more easily and faster, many methods for steganography have been introduced in it. In addition, text is still one of the major forms of communication available to the general public around the world. In this paper, we propose a new email steganography scheme using a dictionary-based compression. In the proposed scheme, a number of email addresses containing a hidden message will be generated using the submitted text. The submitted text is sent to the generated and recipient addresses at the same time. This does not reveal the identity of the recipient of the message, and only the recipient can extract secret message using other email addresses. In the proposed method, two steganography keys are used. Using these two keys increases the security level of the proposed method. Also, the capacity of the proposed method is unlimited, which of course is a great advantage in a steganography method. This unlimited capacity provides high security for the proposed method. Another advantage is that the proposed method is not limited to the type of the cover-text. Initially, the secret message is converted to a bit string by a dictionary. Then the operation of embedding the secret message in the recipient's addresses is done by the steganography keys. The efficiency of steganography algorithms depends on various factors such as lack of detection by the human eye, lack of detection by statistical methods, and capacity. The proposed method does not change the cover-text. Hence, this method is not detectable by humans or statistical methods. The capacity of the proposed method in this research is based on built-in email addresses. As the text of the message increases, the number of emails created increases too. Of course, this increase in the address of the emails created can lead to suspicion of the emails sent. Therefore, the parameter of the number of emails created is also important in the evaluation. In this paper, the efficiency of the proposed method is evaluated based on the two parameters and compared with existing methods. The results of this evaluation show that the proposed method, in addition to providing unlimited capacity in steganography, produces fewer email addresses generated as well as fewer message bits after compression. 59 74 2020/12/162019/08/152020/08/92019/09/282019/07/15 1398/4/24 2021/05/222021/01/102021/01/102020/08/182020/02/3 1398/11/14 محسن رضوانی Mohsen Rezvani دانشگاه صنعتی شاهرود ایران mrezvani@shahroodut.ac.ir منصور فاتح Mansoor Fateh دانشگاه صنعتی شاهرود ایران mansoor_fateh@shahroodut.ac.ir Email Steganography Dictionary Capacity Security نهان‌نگاری رایانامه ظرفیت امنیت لغت‌نامه [1] M. Taleby Ahvanooey, Q. Li, J. Hou, AR. Rajput, C. Yini, "Modern Text Hiding, Text Steganalysis, and Applications: A Comparative Analysis," Entropy, 2019 Apr; 21(4):355.##[2] M. Taleby Ahvanooey, Q. Li, HJ. Shim, Y. Huang, "A comparative analysis of information hiding techniques for copyright protection of text documents," Security and Communication Networks, 2018.##[3] B. Gupta Banik, SK. Bandyopadhyay, "Novel Text Steganography Using Natural Language Processing and Part-of-Speech Tagging", IETE Journal of Research, vo. 13, pp. 1-2, 2018.##[4] NS. Kamaruddin, A. Kamsin, LY. Por, H. Rahman, "A Review of Text Watermarking: Theory, Methods, and Applications," IEEE Access, vol. 6:80, pp. 11-28, 2018.##[5] M. Taleby Ahvanooey, H. Dana Mazraeh, SH. Tabasi, "An innovative technique for web text watermarking (AITW)," Information Security Journal: A Global Perspective, 1;25(4-6):191-6. 2016.##[6] SG. Rizzo, F. Bertini, D. Montesi, C. Stomeo, "Text watermarking in social media," In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, 2017, vol. 31, pp. 208-211, ACM.##[7] MS. Rahman, I. Khalil, X. Yi, "A lossless DNA data hiding approach for data authenticity in mobile cloud based healthcare systems," International Journal of Information Manage-ment, vol. 1, no. 45, pp. 276-88, 2019.##[8] E. Satir and H. Isik, "A Huffman compression based text steganography method," Multimedia tools and applications, vol. 70, no. 3, pp. 2085-2110, 2014.##[9] C.C. Chang, "A reversible data hiding scheme using complementary embedding strategy," Information Sciences, vol. 180, no. 16, pp. 3045-3058, 2010.##[10] E. Satirand H. Isik. "A compression-based text steganography method," Journal of Systems and Software, vol. 85, no. 10, pp. 2385-2394, 2012.##[11] S. Bhattacharyya, P. Indu, and G.Sanyal, "Hiding Data in Text using ASCII Mapping Technology (AMT)," International Journal of Computer Applications, vol. 70, no. 18, 2013.##[12] R. Kumar, A. Malik, S. Singh, B. Kumar, and S. Chand, "A space based reversible high capacity text steganography scheme using font type and style," In International Conference on Computing, Communication and Auto-mation (ICCCA), pp. 1090-1094, 2016.##[13] S.A. Al-Asadi and W.Bhaya, "Text Steganography in Excel Documents Using Color and Type of Fonts," Research Journal of Applied Sciences, vol. 11, no. 10, pp. 1054-1059, 2016.##[14] S. Roy and M.Manasmita, "A novel approach to format based text steganography," In Proceedings of the 2011 International Conference on Communication, Computing & Security, pp. 511-516, 2011.##[15] B.K. Ramakrishnan, P.K.Thandra, and A.V. Srinivasula, "Text steganography: a novel character‐level embedding algorithm using font attribute," Security and Communication Networks, vol. 9, no. 18, pp. 6066-6079, 2016.##[16] A.M. Hamdan and A.Hamarsheh, "AH4S: an algorithm of text in text steganography using the structure of omega network," Security and Communication Networks, vol. 9, no. 18, pp.6004-6016, 2016.##[17] M. Shirali-Shahreza, "Text steganography by changing words spelling," In Advanced Communication Technology, 10th Inter-national Conference on, vol. 3, pp. 1912-1913, 2008.##[18] J. Gardiner, "StegChat: A Synonym-Substitution Based Algorithm for Text Steganography," PhD Thesis, School of Computer Science University of Birmingham, pp. 1-64, 2012.##[19] C.Y. Chang and S. Clark, "Linguistic steganography using automatically generated paraphrases," In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 591-599, 2010.##[20] T.P. Nagarhalli, "A New Approach to SMS Text Steganography using Emoticons," In International Journal of Computer Appli-cations (0975-8887), National Conference on Role of Engineers in Nation Building (NCRENB-14), pp. 1-3, 2014.##[21] M. Garg, "A novel text steganography technique based on html documents," International Journal of Advanced Science and Technology, vol. 35, pp. 129-138, 2011.##[22] A. Majumder and S. Changder, "A novel approach for text steganography: Generating text summary using Reflection Symmetry," Procedia Technology, vol. 10, pp. 112-120, 2013.##[23] L.Y. Por, K. Wong, and K.O. Chee, "UniSpaCh: A text-based data hiding method using Unicode space characters," Journal of Systems and Software, vol. 85, no. 5, pp. 1075-1082, 2012.##[24] R. Kumar, S. Chand, and S. Singh, "An Email based high capacity text steganography scheme using combinatorial compression," In Confluence The Next Generation Information Technology Summit (Confluence), 5th International Conference, pp. 336-339, 2014.##[25] A. Malik, G. Sikka, and H.K. Verma, "A high capacity text steganography scheme based on LZW compression and color coding," Engineering Science and Technology, an International Journal, vol. 20, no. 1, pp.72-79, 2016.##[26] R. Kumar, A. Malik, S. Singh, and S. 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Yini, "Modern Text Hiding, Text Steganalysis, and Applications: A Comparative Analysis," Entropy, 2019 Apr; 21(4):355.##[2] M. Taleby Ahvanooey, Q. Li, HJ. Shim, Y. Huang, "A comparative analysis of information hiding techniques for copyright protection of text documents," Security and Communication Networks, 2018.##[3] B. Gupta Banik, SK. Bandyopadhyay, "Novel Text Steganography Using Natural Language Processing and Part-of-Speech Tagging", IETE Journal of Research, vo. 13, pp. 1-2, 2018.##[4] NS. Kamaruddin, A. Kamsin, LY. Por, H. Rahman, "A Review of Text Watermarking: Theory, Methods, and Applications," IEEE Access, vol. 6:80, pp. 11-28, 2018.##[5] M. Taleby Ahvanooey, H. Dana Mazraeh, SH. Tabasi, "An innovative technique for web text watermarking (AITW)," Information Security Journal: A Global Perspective, 1;25(4-6):191-6. 2016.##[6] SG. Rizzo, F. Bertini, D. Montesi, C. Stomeo, "Text watermarking in social media," In Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, 2017, vol. 31, pp. 208-211, ACM.##[7] MS. Rahman, I. Khalil, X. Yi, "A lossless DNA data hiding approach for data authenticity in mobile cloud based healthcare systems," International Journal of Information Manage-ment, vol. 1, no. 45, pp. 276-88, 2019.##[8] E. Satir and H. Isik, "A Huffman compression based text steganography method," Multimedia tools and applications, vol. 70, no. 3, pp. 2085-2110, 2014.##[9] C.C. Chang, "A reversible data hiding scheme using complementary embedding strategy," Information Sciences, vol. 180, no. 16, pp. 3045-3058, 2010.##[10] E. Satirand H. Isik. "A compression-based text steganography method," Journal of Systems and Software, vol. 85, no. 10, pp. 2385-2394, 2012.##[11] S. Bhattacharyya, P. 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شناسایی رانش مفهوم در نگاره رویداد با استفاده از اطلاعات آماری گونه‌ها Concept drift detection in event logs using statistical information of variants 1 1 در سال‌های اخیر مدیریت فرآیندهای سازمانی (BPM)، به‌دلیل افزایش کارایی سازمان‌ها بسیار مورد توجه قرار گرفته است. استخراج و تحلیل اطلاعات فرآیندهای سازمانی بخش مهمی از این ساختار است؛ اما این فرآیندها در طول زمان پایدار نیستند و به مرور دچار تغییر می‌شوند که به این تغییرات، رانش مفهوم در فرآیند گفته می‌­شود. کشف رانش‌­های مفهوم یکی از چالش‌های موجود در حوزه مدیریت فرآیندهای سازمانی است. در این مقاله الگوریتمی برای شناسایی رانش‌های مفهوم در نگاره رویداد ارائه شده که براساس تحلیل توزیع گونه‌های دنباله در اجرای فرآیند است. در این روش با حرکت دو پنجره روی نگاره رویداد، دو بردار ویژگی از گونه‌های دنباله‌های دو پنجره حاصل و سپس با استفاده از آزمون‌های آماری گونه‌های دو پنجره با یکدیگر مقایسه و در‌نهایت رانش‌ها شناسایی می‌شوند. آزمایش‌های صورت‌گرفته روی پایگاه‌های داده مصنوعی، درستی روش و برتری آن را نسبت به روش‌های پیشین نشان می‌دهند. 2 In recent years, business process management (BPM) has been highly regarded as an improvement in the efficiency and effectiveness of organizations. Extracting and analyzing information on business processes is an important part of this structure. But these processes are not sustainable over time and may change for a variety of reasons, such as the environment, human resources, capital market changes, seasonal, and climate changes. These changes in business processes are referred to as concept drift in event logs. The discovery of concept drifts is one of the challenges in business process management. These drifts may occur suddenly, gradually, periodically, or incrementally. This paper proposes an algorithm for identifying sudden concept drifts in event logs that are created by BPM. Each execution of the process instance follows a specific path in the process model called a trace, all traces that follow the same path in process model are called a variant. The proposed algorithm is based on the distribution of trace variants in the execution of processes. In this method, by moving two sliding windows on the event log, two feature vectors are derived from the two windows trace variants, these windows are named reference and detection windows. Then variants of the two windows are compared by applying statistical G-test and finally the drifts are identified.  In statistics, G-test is likelihood-ratio or maximum likelihood statistical significance test. Experiments on artificial databases show the correctness of the method and its superiority to the previous methods. In the proposed method, the detection accuracy is 0.06% better than state-of-the-art methods on average 75 86 2020/12/162019/08/152020/08/92019/09/282019/07/152019/07/26 1398/5/4 2021/05/222021/01/102021/01/102020/08/182020/02/32021/05/23 1400/3/2 فرشته جوادزاده fershteh javadzadeh دانشگاه گلستان ایران fereshteh.jvz@gmail.com مهدی یعقوبی mehdi yaghoubi دانشگاه گلستان ایران mehdi.yaghoubi@gmail.com سهیلا کرباسی soheila karbasi دانشگاه گلستان ایران s.karbasi@gu.ac.ir Concept drift event log process mining business processes variant رانش مفهوم نگاره رویداد فرآیند کاوی فرآیندهای سازمانی گونه [1] W. Van Der Aalst, "Process mining: discovery, conformance and enhancement of business processes," Springer, vol. 2, pp.157-158, 2011.##[2] W. M. P. Aalst, "Process Mining: Overview and Opportunities," ACM Trans. Manag. Inf. Syst., vol. 3, no. 2, pp. 1-17, 2012.##[3] W. Van Der Aalst, A. Adriansyah, A. K. A. De Medeiros, F. Arcieri, T. Baier, T. Blickle, J. C. Bose, P. Van Den Brand, R. Brandtjen, J. Buijs, and others, "Process mining manifesto," in International Conference on Business Process Management, 2011, pp. 169-194.##[4] R. P. J. C. Bose, W. M. P. van der Aalst, I. Zliobaite, and M. Pechenizkiy, "Dealing with concept drifts in process mining.," IEEE Trans. neural networks Learn. Syst., vol. 25, no. 1, pp. 154-71, 2014.##[5] R. Klinkenberg and T. Joachims, "Detecting Concept Drift with Support Vector Machines.," in ICML, 2000, pp. 487-494.##[6] B. R. P. J. C. . b, V. D. A. W.M.P.a, Ž. I.a, and P. M.a, "Handling concept drift in process mining," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, vol. 6741 LNCS, pp. 391-405.##[7] J. Martjushev, R. P. Jagadeesh Chandra Bose, and W. M. P. van der Aalst, "Change point detection and dealing with gradual and multi-order dynamics in process mining," in Lecture Notes in Business Information Processing, 2015, vol. 229, pp. 161-178.##[8] A. Maaradji, M. Dumas, M. La Rosa, and A. Ostovar, "Fast and accurate business process drift detection," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9253, pp. 406-422.##[9] T. Li, T. He, Z. Wang, Y. Zhang, and D. Chu, "Unraveling Process Evolution by Handling Concept Drifts in Process Mining," in Proceedings - 2017 IEEE 14th International Conference on Services Computing, SCC 2017, 2017, pp. 442-449.##[10] A. Seeliger, T. Nolle, and M. Mühlhäuser, "Detecting Concept Drift in Processes using Graph Metrics on Process Graphs," in Proceedings of the 9th Conference on Subject-Oriented Business Process Management, 2017, pp. 1-10.##[11] P. Harremoës and G. Tusnády, "Information divergence is more $χ$ 2-distributed than the $χ$ 2-statistics," in 2012 IEEE International Symposium on Information Theory Proceedings, 2012, pp. 533-537.##[12] R. Nuzzo, "Scientific method: statistical errors," Nat. News, vol. 506, no. 7487, pp. 150, 2014.##[13] M. Dumas, M. La Rosa, J. Mendling, H. A. Reijers, and others, Fundamentals of business process management, vol. 1. Springer, 2013.##[14] B. Weber, M. Reichert, and S. Rinderle-Ma, "Change patterns and change support features--enhancing flexibility in process-aware information systems," Data Knowl. Eng., vol. 66, no. 3, pp. 438-466, 2008.##[15] S.-S. Ho, "A martingale framework for concept change detection in time-varying data streams," in Proceedings of the 22nd international conference on Machine learning, 2005, pp. 321-327.##[16] A. Ostovar, M. Abderrahmane, M. La Rosa, A. H. ter Hofstede, and B. F. van Dongen., "Detecting drift from event streams of unpredictable business processes," in International Conference on Conceptual Modeling, 2016, pp. 330-346.##[17] R. Accorsi and T. Stocker, "Discovering workflow changes with time-based trace clustering," in International Symposium on Data-Driven Process Discovery and Analysis, 2011, pp. 154-168.##[18] B. Hompes, J. C. A. M. Buijs, W. M. P. van der Aalst, P. Dixit, and H. Buurman, "Detecting Change in Processes Using Comparative Trace Clustering.," in SIMPDA, 2015, pp. 95-108.##[19] J. Carmona and R. Gavalda, "Online techniques for dealing with concept drift in process mining," in International Symposium on Intelligent Data Analysis, 2012, pp. 90-102.##[20] F. Khojasteh, M. Kahani and B. Behkamal, "Concept drift detection in business process logs using deep learning" Signal and Data Processing, vol. 46, no. 4, pp. 33-48, 2021##[1] W. Van Der Aalst, "Process mining: discovery, conformance and enhancement of business processes," Springer, vol. 2, pp.157-158, 2011.##[2] W. M. P. Aalst, "Process Mining: Overview and Opportunities," ACM Trans. Manag. Inf. Syst., vol. 3, no. 2, pp. 1-17, 2012.##[3] W. Van Der Aalst, A. Adriansyah, A. K. A. De Medeiros, F. Arcieri, T. Baier, T. Blickle, J. C. Bose, P. Van Den Brand, R. Brandtjen, J. Buijs, and others, "Process mining manifesto," in International Conference on Business Process Management, 2011, pp. 169-194.##[4] R. P. J. C. Bose, W. M. P. van der Aalst, I. Zliobaite, and M. Pechenizkiy, "Dealing with concept drifts in process mining.," IEEE Trans. neural networks Learn. Syst., vol. 25, no. 1, pp. 154-71, 2014.##[5] R. Klinkenberg and T. Joachims, "Detecting Concept Drift with Support Vector Machines.," in ICML, 2000, pp. 487-494.##[6] B. R. P. J. C. . b, V. D. A. W.M.P.a, Ž. I.a, and P. M.a, "Handling concept drift in process mining," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, vol. 6741 LNCS, pp. 391-405.##[7] J. Martjushev, R. P. Jagadeesh Chandra Bose, and W. M. P. van der Aalst, "Change point detection and dealing with gradual and multi-order dynamics in process mining," in Lecture Notes in Business Information Processing, 2015, vol. 229, pp. 161-178.##[8] A. Maaradji, M. Dumas, M. La Rosa, and A. Ostovar, "Fast and accurate business process drift detection," in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9253, pp. 406-422.##[9] T. Li, T. He, Z. Wang, Y. Zhang, and D. Chu, "Unraveling Process Evolution by Handling Concept Drifts in Process Mining," in Proceedings - 2017 IEEE 14th International Conference on Services Computing, SCC 2017, 2017, pp. 442-449.##[10] A. Seeliger, T. Nolle, and M. Mühlhäuser, "Detecting Concept Drift in Processes using Graph Metrics on Process Graphs," in Proceedings of the 9th Conference on Subject-Oriented Business Process Management, 2017, pp. 1-10.##[11] P. Harremoës and G. Tusnády, "Information divergence is more $χ$ 2-distributed than the $χ$ 2-statistics," in 2012 IEEE International Symposium on Information Theory Proceedings, 2012, pp. 533-537.##[12] R. Nuzzo, "Scientific method: statistical errors," Nat. News, vol. 506, no. 7487, pp. 150, 2014.##[13] M. Dumas, M. La Rosa, J. Mendling, H. A. Reijers, and others, Fundamentals of business process management, vol. 1. Springer, 2013.##[14] B. Weber, M. Reichert, and S. Rinderle-Ma, "Change patterns and change support features--enhancing flexibility in process-aware information systems," Data Knowl. Eng., vol. 66, no. 3, pp. 438-466, 2008.##[15] S.-S. Ho, "A martingale framework for concept change detection in time-varying data streams," in Proceedings of the 22nd international conference on Machine learning, 2005, pp. 321-327.##[16] A. Ostovar, M. Abderrahmane, M. La Rosa, A. H. ter Hofstede, and B. F. van Dongen., "Detecting drift from event streams of unpredictable business processes," in International Conference on Conceptual Modeling, 2016, pp. 330-346.##[17] R. Accorsi and T. Stocker, "Discovering workflow changes with time-based trace clustering," in International Symposium on Data-Driven Process Discovery and Analysis, 2011, pp. 154-168.##[18] B. Hompes, J. C. A. M. Buijs, W. M. P. van der Aalst, P. Dixit, and H. Buurman, "Detecting Change in Processes Using Comparative Trace Clustering.," in SIMPDA, 2015, pp. 95-108.##[19] J. Carmona and R. Gavalda, "Online techniques for dealing with concept drift in process mining," in International Symposium on Intelligent Data Analysis, 2012, pp. 90-102.##[20] F. Khojasteh, M. Kahani and B. Behkamal, "Concept drift detection in business process logs using deep learning" Signal and Data Processing, vol. 46, no. 4, pp. 33-48, 2021##[20] فاطمه خجسته، محسن کاهانی و بهشید بهکمال "شناسایی رانش مفهومی در نگاره‌های فرایند کسب‌وکار با استفاده از یادگیری عمیق." پردازش علائم و داده‌ها، شماره 4، نسخه 46، صفحه 33-48، 1399 ##

ارائه الگوریتمی جدید برای تشخیص اجتماع با استفاده از یادگیری تقویتی چندعاملی A Multiagent Reinforcement Learning algorithm to solve the Community Detection Problem 1 1 مسأله تشخیص اجتماع، یکی از مسائل چالش‌برانگیز بهینه‌سازی است که شامل جستجو برای اجتماعاتی است که به یک شبکه یا گراف تعلق دارند و گره‌های عضو هر یک از آن‌ها دارای ویژگی‌های مشترک هستند، که تشخیص ویژگی‌های جدید یا روابط خاص در شبکه را ممکن می‌سازند. اگرچه برای مسأله تشخیص اجتماع الگوریتم‌های متعددی ارائه‌شده است، اما بسیاری از آن‌ها در مواجه با شبکه‌های با مقیاس بزرگ قابل‌استفاده نیستند و از هزینه محاسباتی بسیار بالایی برخوردارند. در این مقاله، الگوریتم جدیدی مبتنی بر یادگیری تقویتی چندعاملی برای تشخیص اجتماع در شبکه‌های پیچیده ارائه خواهیم کرد که در آن، هر عامل یک موجودیت مستقل با پارامترهای یادگیری متفاوت هستند و بر اساس همکاری بین عامل‌ها، الگوریتم پیشنهادی به‌صورت تکرارشونده و بر اساس مکانیزم یادگیری تقویتی، به جستجوی اجتماعات بهینه می‌پردازد. کارایی الگوریتم پیشنهادی را بر روی چهار شبکه واقعی و تعدادی شبکه مصنوعی ارزیابی شده است، و با تعدادی از الگوریتم‌های مشهور در این زمینه مقایسه می‌کنیم. بر اساس ارزیابی‌ انجام‌گرفته، الگوریتم پیشنهادی علاوه بر دقت بالا در تشخیص اجتماع، از سرعت و پایداری مناسبی برخوردار است و قابلیت رقابت و حتی غلبه بر الگوریتم‌های مطرح در زمینه تشخیص اجتماع را نیز داشته و نتایج الگوریتم پیشنهادی بر اساس معیارهای Q-ماجولاریتی و NMI متوسط بر روی شبکه‌های واقعی و مصنوعی به‌ترتیب 33/12%، 85/9% و بیش از 21 % بهتر از الگوریتم‌های مورد مقایسه است. 2 Recent researches show that diverse systems in many different areas can be represented as complex networks. Examples of these include the Internet, social networks and so on. In each case, the system can be modeled as a complex and very large network consisting of a large number of entities and associations between them. Most of these networks are generally sparse in global yet dense in local. They have vertices in a group structure and the vertices within a group have higher density of edges while vertices among groups have lower density of edges. Such a structure is called community and is one of the important features of the network and is able to reveal many hidden characteristics of the networks. Today, community detection is used to improve the efficiency of search engines and discovery of terrorist organizations on the World Wide Web. Community detection is a challenging NP-hard optimization problem that consists of searching for communities. It is assumed that the nodes of the same community share some properties that enable the detection of new characteristics or functional relationships in a network. Although there are many algorithms developed for community detection, most of them are unsuitable when dealing with large networks due to their computational cost. Nowadays, multiagent systems have been used to solve different problems, such as constraint satisfaction problems and combinatorial optimization problems with satisfactory results. In this paper, a new multiagent reinforcement learning algorithm is proposed for community detection in complex networks. Each agent in the multiagent system is an autonomous entity with different learning parameters. Based on the cooperation among the learning agents and updating the action probabilities of each agent, the algorithm interactively will identify a set of communities in the input network that are more densely connected than other communities. In other words, some independent agents interactively attempt to identify communities and evaluate the quality of the communities found at each stage by the normalized cut as objective function; then, the probability vectors of the agents are updated based on the results of the evaluation. If the quality of the community found by an agent in each of the stages is better than all the results produced so far, then it is referred to as the successful agent and the other agents will update their probability vectors based on the result of the successful agent. In the experiments, the performance of the proposed algorithm is validated on four real-world benchmark networks: the Karate club network, Dolphins network, Political books network and College football network, and synthetic LFR benchmark graphs with scales of 1000 and 5000 nodes. LFR networks are suitable for systematically measuring the property of an algorithm. Experimental results show that proposed approach has a good performance and is able to find suitable communities in large and small scale networks and is capable of detecting the community in complex networks In terms of speed, precision and stability. Moreover, according to the systematic comparison of the results obtained by the proposed algorithm with four state-of-the-art community detection algorithms, our algorithm outperforms the these algorithms in terms of modularity and NMI; also, it can detect communities in small and large scale networks with high speed, accuracy, and stability, where it is capable of managing large-scale networks up to 5000 nodes. 87 100 2020/12/162019/08/152020/08/92019/09/282019/07/152019/07/262019/10/15 1398/7/23 2021/05/222021/01/102021/01/102020/08/182020/02/32021/05/232021/12/6 1400/9/15 میر محمد علیپور Mir Mohammad Alipour گروه مهندسی کامپیوتر، دانشگاه بناب ایران alipour@ubonab.ac.ir محسن عبدالحسین زاده Mohsen Abdolhosseinzadeh گروه ریاضی، دانشکده علوم پایه، دانشگاه بناب ایران mohsen.ab@ubonab.ac.ir Complex networks Community detection Multiagent systems Reinforcement learning Modularity Q شبکه‌های پیچیده تشخیص اجتماع سیستم‌های چندعاملی یادگیری تقویتی Q-ماجولاریتی [1] D. J. Watts and S. H. Strogatz, "Collective dynamics of 'small-world'networks," nature, vol. 393, no. 6684, pp. 440, 1998.##[2] S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D.-U. Hwang, "Complex networks: Structure and dynamics," Physics reports, vol. 424, no. 4-5, pp. 175-308, 2006.##[3] M. E. Newman, "The structure and function of complex networks," SIAM review, vol. 45, no. 2, pp. 167-256, 2003.##[4] S. Wasserman and K. Faust, Social network analysis: Methods and applications. Cambridge university press, 1994.##[5] R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon, "Network motifs: simple building blocks of complex networks," Science, vol. 298, no. 5594, pp. 824-827, 2002.##[6] U. Brandes et al., "On modularity clustering," IEEE transactions on knowledge and data engineering, vol. 20, no. 2, pp. 172-188, 2007.##[7] J. Liu, W. Zhong, and L. Jiao, "A multiagent evolutionary algorithm for combinatorial optimization problems," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 40, no. 1, pp. 229-240, 2009.##[8] J. Liu, W. Zhong, and L. Jiao, "A multiagent evolutionary algorithm for constraint satisfaction problems," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 36, no. 1, pp. 54-73, 2006.##[9] M. M. Alipour and S. N. Razavi, "A new multiagent reinforcement learning algorithm to solve the symmetric traveling salesman problem," Multiagent and Grid Systems, vol. 11, no. 2, pp. 107-119, 2015.##[10] M. M. Alipour, S. N. Razavi, M. R. F. Derakhshi, and M. A. Balafar, "A hybrid algorithm using a genetic algorithm and multiagent reinforcement learning heuristic to solve the traveling salesman problem," Neural Computing and Applications, pp. 1-17, 2017.##[11] M. M. Alipour and M. Abdolhosseinzadeh, "A multiagent reinforcement learning algorithm to solve the maximum independent set problem," Multiagent and Grid Systems, vol. 16, no. 1, pp. 101-115, 2020.##[12] M. E. Newman, "Modularity and community structure in networks," Proceedings of the national academy of sciences, vol. 103, no. 23, pp. 8577-8582, 2006.##[13] A. Clauset, M. E. Newman, and C. Moore, "Finding community structure in very large networks," Physical review E, vol. 70, no. 6, p. 066111, 2004.##[14] J. M. Kumpula, J. Saramäki, K. Kaski, and J. Kertész, "Limited resolution and multiresolution methods in complex network community detection," Fluctuation and Noise Letters, vol. 7, no. 03, pp. L209-L214, 2007.##[15] S. Fortunato, "Community detection in graphs," Physics reports, vol. 486, no. 3-5, pp. 75-174, 2010.##[16] L. Donetti and M. A. Munoz, "Detecting network communities: a new systematic and efficient algorithm," Journal of Statistical Mechanics: Theory and Experiment, vol. 2004, no. 10, pp. P10012, 2004.##[17] M. Girvan and M. E. Newman, "Community structure in social and biological networks," Proceedings of the national academy of sciences, vol. 99, no. 12, pp. 7821-7826, 2002.##[18] M. E. Newman and M. Girvan, "Finding and evaluating community structure in networks," Physical review E, vol. 69, no. 2, pp. 026113, 2004.##[19] F. Radicchi, C. Castellano, F. Cecconi, V. Loreto, and D. Parisi, "Defining and identifying communities in networks," Proceedings of the national academy of sciences, vol. 101, no. 9, pp. 2658-2663, 2004.##[20] L. Hagen and A. B. Kahng, "A new approach to effective circuit clustering," in ICCAD, 1992, vol. 92, pp. 422-427.##[21] P. De Meo, E. Ferrara, G. Fiumara, and A. Provetti, "Mixing local and global information for community detection in large networks," Journal of Computer and System Sciences, vol. 80, no. 1, pp. 72-87, 2014.##[22] J. Cao, Z. Bu, Y. Wang, H. Yang, J. Jiang, and H.-J. Li, "Detecting prosumer-community groups in smart grids from the multiagent perspective," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 8, pp. 1652-1664, 2019.##[23] C.-K. Han, S.-F. Cheng, and P. Varakantham, "A Homophily-Free Community Detection Framework for Trajectories with Delayed Responses," in Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019: International Foundation for Autonomous Agents and Multiagent Systems, pp. 2003-2005.##[24] X. Feng and X. Yang, "Fast convergent average consensus of multiagent systems based on community detection algorithm," Advances in Difference Equations, vol. 2018, no. 1, pp. 1-13, 2018.##[25] P. Pons and M. Latapy, "Computing communities in large networks using random walks," J. Graph Algorithms Appl., vol. 10, no. 2, pp. 191-218, 2006.##[26] P. Ronhovde and Z. Nussinov, "Multiresolution community detection for megascale networks by information-based replica correlations," Physical Review E, vol. 80, no. 1, p. 016109, 2009.##[27] M. Zhou and J. Liu, "A memetic algorithm for enhancing the robustness of scale-free networks against malicious attacks," Physica A: Statistical Mechanics and its Applications, vol. 410, pp. 131-143, 2014.##[28] T. N. Bui and B. R. Moon, "Genetic algorithm and graph partitioning," IEEE Transactions on computers, vol. 45, no. 7, pp. 841-855, 1996.##[29] M. Tasgin, A. Herdagdelen, and H. Bingol, "Community detection in complex networks using genetic algorithms," arXiv preprint arXiv:0711.0491, 2007.##[30] C. Pizzuti, "Ga-net: A genetic algorithm for community detection in social networks," in International conference on parallel problem solving from nature, 2008: Springer, pp. 1081-1090.##[31] A. Gog, D. Dumitrescu, and B. Hirsbrunner, "Community detection in complex networks using collaborative evolutionary algorithms," in European Conference on Artificial Life, 2007: Springer, pp. 886-894.##[32] M. Gong, B. Fu, L. Jiao, and H. Du, "Memetic algorithm for community detection in networks," Physical Review E, vol. 84, no. 5, p. 056101, 2011.##[33] J. Liu, W. Zhong, H. A. Abbass, and D. G. Green, "Separated and overlapping community detection in complex networks using multiobjective evolutionary algorithms," in IEEE Congress on Evolutionary Computation, 2010: IEEE, pp. 1-7.##[34] X. Liu and T. Murata, "Advanced modularity-specialized label propagation algorithm for detecting communities in networks," Physica A: Statistical Mechanics and its Applications, vol. 389, no. 7, pp. 1493-1500, 2010.##[35] J. Duch and A. Arenas, "Community detection in complex networks using extremal optimization," Physical review E, vol. 72, no. 2, p. 027104, 2005.##[36] B. Yang and D.-Y. Liu, "Force-based incremental algorithm for mining community structure in dynamic network," Journal of Computer Science and Technology, vol. 21, no. 3, pp. 393-400, 2006.##[37] I. Gunes and H. Bingol, "Community detection in complex networks using agents," arXiv preprint cs/0610129, 2006.##[38] Z. Li and J. Liu, "A multi-agent genetic algorithm for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, vol. 449, pp. 336-347, 2016.##[39] J. Huang, B. Yang, D. Jin, and Y. Yang, "Decentralized mining social network communities with agents," Mathematical and Computer Modelling, vol. 57, no. 11-12, pp. 2998-3008, 2013.##[40] G. Palla, I. Derényi, I. Farkas, and T. Vicsek, "Uncovering the overlapping community structure of complex networks in nature and society," nature, vol. 435, no. 7043, p. 814, 2005.##[41] S. J. Russell and P. Norvig, Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited, 2016.##[42] R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction. Cambridge: MIT press, 1998.##[43] L. P. Kaelbling, M. L. Littman, and A. W. Moore, "Reinforcement learning: A survey," Journal of Artificial Intelligence Research, vol. 4, pp. 237-285, 1996.##[44] P. Stone and M. Veloso, " Multiagent systems: A survey from the machine learning perspective," Autonomous Robots, vol. 8, no. 3, pp. 345-383, 2000.##[45] S. Sen and G. Weiss, "Learning in multiagent systems," in Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, G. Weiss Ed.: MIT Press, 1999, ch. 6, pp. 259-298.##[46] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. MIT Press, 1998.##[47] I. S. Dhillon, Y. Guan, and B. Kulis, "Kernel k-means: spectral clustering and normalized cuts," in Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, 2004: ACM, pp. 551-556.##[48] J. Shi and J. Malik, "Normalized cuts and image segmentation," Departmental Papers (CIS), pp. 107, 2000.##[49] W. W. Zachary, "An information flow model for conflict and fission in small groups," Journal of anthropological research, vol. 33, no. 4, pp. 452-473, 1977.##[50] D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson, "The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations," Behavioral Ecology and Sociobiology, vol. 54, no. 4, pp. 396-405, 2003.##[51] V. Krebs, "Books about us politics," unpublished, http://www.orgnet.com, 2004.##[52] A. Lancichinetti, S. Fortunato, and F. Radicchi, "Benchmark graphs for testing community detection algorithms," Physical review E, vol. 78, no. 4, p. 046110, 2008.##[53] L. Danon, A. Diaz-Guilera, J. Duch, and A. Arenas, "Comparing community structure identification," Journal of Statistical Mechanics: Theory and Experiment, vol. 2005, no. 09, pp. P09008, 2005.##[54] M. Tokic, F. Schwenker, and G. Palm, "Meta-learning of exploration and exploitation parameters with replacing eligibility traces," presented at the In IAPR International Workshop on Partially Supervised Learning (pp. 68-79). Springer Berlin Heidelberg, 2013, May.##[55] K. Kobayashi, H. Mizoue, T. Kuremoto, and M. Obayashi, "A meta-learning method based on temporal difference error," presented at the In International Conference on Neural Information Processing (pp. 530-537). Springer Berlin Heidelberg, 2009, December.##[1] D. J. Watts and S. H. Strogatz, "Collective dynamics of 'small-world'networks," nature, vol. 393, no. 6684, pp. 440, 1998.##[2] S. Boccaletti, V. Latora, Y. Moreno, M. Chavez, and D.-U. Hwang, "Complex networks: Structure and dynamics," Physics reports, vol. 424, no. 4-5, pp. 175-308, 2006.##[3] M. E. Newman, "The structure and function of complex networks," SIAM review, vol. 45, no. 2, pp. 167-256, 2003.##[4] S. Wasserman and K. Faust, Social network analysis: Methods and applications. Cambridge university press, 1994.##[5] R. Milo, S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon, "Network motifs: simple building blocks of complex networks," Science, vol. 298, no. 5594, pp. 824-827, 2002.##[6] U. Brandes et al., "On modularity clustering," IEEE transactions on knowledge and data engineering, vol. 20, no. 2, pp. 172-188, 2007.##[7] J. Liu, W. Zhong, and L. Jiao, "A multiagent evolutionary algorithm for combinatorial optimization problems," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 40, no. 1, pp. 229-240, 2009.##[8] J. Liu, W. Zhong, and L. Jiao, "A multiagent evolutionary algorithm for constraint satisfaction problems," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 36, no. 1, pp. 54-73, 2006.##[9] M. M. Alipour and S. N. Razavi, "A new multiagent reinforcement learning algorithm to solve the symmetric traveling salesman problem," Multiagent and Grid Systems, vol. 11, no. 2, pp. 107-119, 2015.##[10] M. M. Alipour, S. N. Razavi, M. R. F. Derakhshi, and M. A. Balafar, "A hybrid algorithm using a genetic algorithm and multiagent reinforcement learning heuristic to solve the traveling salesman problem," Neural Computing and Applications, pp. 1-17, 2017.##[11] M. M. Alipour and M. Abdolhosseinzadeh, "A multiagent reinforcement learning algorithm to solve the maximum independent set problem," Multiagent and Grid Systems, vol. 16, no. 1, pp. 101-115, 2020.##[12] M. E. Newman, "Modularity and community structure in networks," Proceedings of the national academy of sciences, vol. 103, no. 23, pp. 8577-8582, 2006.##[13] A. Clauset, M. E. Newman, and C. Moore, "Finding community structure in very large networks," Physical review E, vol. 70, no. 6, p. 066111, 2004.##[14] J. M. Kumpula, J. Saramäki, K. Kaski, and J. Kertész, "Limited resolution and multiresolution methods in complex network community detection," Fluctuation and Noise Letters, vol. 7, no. 03, pp. L209-L214, 2007.##[15] S. Fortunato, "Community detection in graphs," Physics reports, vol. 486, no. 3-5, pp. 75-174, 2010.##[16] L. Donetti and M. A. Munoz, "Detecting network communities: a new systematic and efficient algorithm," Journal of Statistical Mechanics: Theory and Experiment, vol. 2004, no. 10, pp. P10012, 2004.##[17] M. Girvan and M. E. Newman, "Community structure in social and biological networks," Proceedings of the national academy of sciences, vol. 99, no. 12, pp. 7821-7826, 2002.##[18] M. E. Newman and M. Girvan, "Finding and evaluating community structure in networks," Physical review E, vol. 69, no. 2, pp. 026113, 2004.##[19] F. Radicchi, C. Castellano, F. Cecconi, V. Loreto, and D. Parisi, "Defining and identifying communities in networks," Proceedings of the national academy of sciences, vol. 101, no. 9, pp. 2658-2663, 2004.##[20] L. Hagen and A. B. Kahng, "A new approach to effective circuit clustering," in ICCAD, 1992, vol. 92, pp. 422-427.##[21] P. De Meo, E. Ferrara, G. Fiumara, and A. Provetti, "Mixing local and global information for community detection in large networks," Journal of Computer and System Sciences, vol. 80, no. 1, pp. 72-87, 2014.##[22] J. Cao, Z. Bu, Y. Wang, H. Yang, J. Jiang, and H.-J. Li, "Detecting prosumer-community groups in smart grids from the multiagent perspective," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 8, pp. 1652-1664, 2019.##[23] C.-K. Han, S.-F. Cheng, and P. Varakantham, "A Homophily-Free Community Detection Framework for Trajectories with Delayed Responses," in Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019: International Foundation for Autonomous Agents and Multiagent Systems, pp. 2003-2005.##[24] X. Feng and X. Yang, "Fast convergent average consensus of multiagent systems based on community detection algorithm," Advances in Difference Equations, vol. 2018, no. 1, pp. 1-13, 2018.##[25] P. Pons and M. Latapy, "Computing communities in large networks using random walks," J. Graph Algorithms Appl., vol. 10, no. 2, pp. 191-218, 2006.##[26] P. Ronhovde and Z. Nussinov, "Multiresolution community detection for megascale networks by information-based replica correlations," Physical Review E, vol. 80, no. 1, p. 016109, 2009.##[27] M. Zhou and J. Liu, "A memetic algorithm for enhancing the robustness of scale-free networks against malicious attacks," Physica A: Statistical Mechanics and its Applications, vol. 410, pp. 131-143, 2014.##[28] T. N. Bui and B. R. Moon, "Genetic algorithm and graph partitioning," IEEE Transactions on computers, vol. 45, no. 7, pp. 841-855, 1996.##[29] M. Tasgin, A. Herdagdelen, and H. Bingol, "Community detection in complex networks using genetic algorithms," arXiv preprint arXiv:0711.0491, 2007.##[30] C. Pizzuti, "Ga-net: A genetic algorithm for community detection in social networks," in International conference on parallel problem solving from nature, 2008: Springer, pp. 1081-1090.##[31] A. Gog, D. Dumitrescu, and B. Hirsbrunner, "Community detection in complex networks using collaborative evolutionary algorithms," in European Conference on Artificial Life, 2007: Springer, pp. 886-894.##[32] M. Gong, B. Fu, L. Jiao, and H. Du, "Memetic algorithm for community detection in networks," Physical Review E, vol. 84, no. 5, p. 056101, 2011.##[33] J. Liu, W. Zhong, H. A. Abbass, and D. G. Green, "Separated and overlapping community detection in complex networks using multiobjective evolutionary algorithms," in IEEE Congress on Evolutionary Computation, 2010: IEEE, pp. 1-7.##[34] X. Liu and T. Murata, "Advanced modularity-specialized label propagation algorithm for detecting communities in networks," Physica A: Statistical Mechanics and its Applications, vol. 389, no. 7, pp. 1493-1500, 2010.##[35] J. Duch and A. Arenas, "Community detection in complex networks using extremal optimization," Physical review E, vol. 72, no. 2, p. 027104, 2005.##[36] B. Yang and D.-Y. Liu, "Force-based incremental algorithm for mining community structure in dynamic network," Journal of Computer Science and Technology, vol. 21, no. 3, pp. 393-400, 2006.##[37] I. Gunes and H. Bingol, "Community detection in complex networks using agents," arXiv preprint cs/0610129, 2006.##[38] Z. Li and J. Liu, "A multi-agent genetic algorithm for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, vol. 449, pp. 336-347, 2016.##[39] J. Huang, B. Yang, D. Jin, and Y. Yang, "Decentralized mining social network communities with agents," Mathematical and Computer Modelling, vol. 57, no. 11-12, pp. 2998-3008, 2013.##[40] G. Palla, I. Derényi, I. Farkas, and T. Vicsek, "Uncovering the overlapping community structure of complex networks in nature and society," nature, vol. 435, no. 7043, p. 814, 2005.##[41] S. J. Russell and P. Norvig, Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited, 2016.##[42] R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction. Cambridge: MIT press, 1998.##[43] L. P. Kaelbling, M. L. Littman, and A. W. Moore, "Reinforcement learning: A survey," Journal of Artificial Intelligence Research, vol. 4, pp. 237-285, 1996.##[44] P. Stone and M. Veloso, " Multiagent systems: A survey from the machine learning perspective," Autonomous Robots, vol. 8, no. 3, pp. 345-383, 2000.##[45] S. Sen and G. Weiss, "Learning in multiagent systems," in Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence, G. Weiss Ed.: MIT Press, 1999, ch. 6, pp. 259-298.##[46] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction. MIT Press, 1998.##[47] I. S. Dhillon, Y. Guan, and B. Kulis, "Kernel k-means: spectral clustering and normalized cuts," in Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, 2004: ACM, pp. 551-556.##[48] J. Shi and J. Malik, "Normalized cuts and image segmentation," Departmental Papers (CIS), pp. 107, 2000.##[49] W. W. Zachary, "An information flow model for conflict and fission in small groups," Journal of anthropological research, vol. 33, no. 4, pp. 452-473, 1977.##[50] D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson, "The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations," Behavioral Ecology and Sociobiology, vol. 54, no. 4, pp. 396-405, 2003.##[51] V. Krebs, "Books about us politics," unpublished, http://www.orgnet.com, 2004.##[52] A. Lancichinetti, S. Fortunato, and F. Radicchi, "Benchmark graphs for testing community detection algorithms," Physical review E, vol. 78, no. 4, p. 046110, 2008.##[53] L. Danon, A. Diaz-Guilera, J. Duch, and A. Arenas, "Comparing community structure identification," Journal of Statistical Mechanics: Theory and Experiment, vol. 2005, no. 09, pp. P09008, 2005.##[54] M. Tokic, F. Schwenker, and G. Palm, "Meta-learning of exploration and exploitation parameters with replacing eligibility traces," presented at the In IAPR International Workshop on Partially Supervised Learning (pp. 68-79). Springer Berlin Heidelberg, 2013, May.##[55] K. Kobayashi, H. Mizoue, T. Kuremoto, and M. Obayashi, "A meta-learning method based on temporal difference error," presented at the In International Conference on Neural Information Processing (pp. 530-537). Springer Berlin Heidelberg, 2009, December. ##

استخراج رابطه مبتنی بر تعبیه لغات با فرآیند جمع‌سپاری Relation extraction based on word embedding with Crowdsourcing Process 1 1 برای انجام مطالعات داده‌کاوی، تاحدودی به‌دلیل پیچیده‌بودن فرآیند انتخاب ویژگی در کار مورد نظر، نیاز داریم تا بخشی از برچسب‌زنی را به کارگران در فعالیت جمع‌­سپاری واگذار کنیم. فرآیند واگذاری کارهای داده‌کاوی به کاربران، اغلب به‌وسیله سامانه‌های نرم‌افزاری و بدون اطلاع دقیق از موقعیت سنی یا جغرافیای محل سکونت کاربران صورت می‌گیرد. عدم اطمینان از عملکرد کاربران مجازی در جمع­‌سپاری، میزان صحت اطلاعات دریافتی را کاهش می‌دهد. در این مقاله پیشنهاد داده‌ایم تا با استفاده از روش‌های ایجاد انگیزش، تعدادی از مردم را در محلی جمع و از آنها در جهت وظایف جمع­‌سپاری استفاده کنیم. افزایش دقت در اعلام نتایج به‌دلیل حضور فیزیکی، سرعت بالا در گرفتن نتایج با دقت بالا در زمان تعیین‌شده، تحصیلات مناسب شرکت‌کنندگان در فعالیت و بومی‌بودن طرح اجرایی از ویژگی­‌های این پژوهش هستند. در این پژوهش یک کار یادگیری ماشین انجام شد تا بتوانیم در ضمن آن فعالیت­‌های جمع‌سپاری را با الگوریتم‌­های شبکه عصبی عمیق ترکیب نماییم.  وظیفه کلاس‌بندی برای تعبیه لغات به‌صورت الگوریتمی و تلفیقی با کمک جمع‌سپاری انجام می‌­شود. روش پیشنهادی با افزودن داده‌های جمع‌سپار به داده‌های قبلی و تغییرات در مدل تعبیه لغات ترکیبی گلاو و وردتووک توانست نتایج مناسبی را  در استخراج ویژگی به‌دست بیاورد. 2 For data mining studies, due to the complexity of doing feature selection process in tasks by hand, we need to send some of labeling to the workers with crowdsourcing activities. The process of outsourcing data mining tasks to users is often handled by software systems without enough knowledge of the age or geography of the users' residence. We use convolutional neural network, for doing classification in six classes: USAGE, TOPIC, COMPARE, MODEL-FEATURE, RESULT and PART-WHOLE. This article extracts the data from the abstract of 450 scientific articles and it is a total of 835 relations. One hundred of these abstracts have been selected by the crowdsourcing. Classification results in this article have been done with a slight improvement in accuracy. In this study, we computed the classification results on a combination of vocabulary vectors with using of 450 abstract relation data (100 crowd source datasets with 350 standards). The results of the implementation of the classification algorithm give us performance improvement. This paper uses the population power to perform preparing data mining works. The proposed method by adding crowdsource data to the previous data was able to obtain better results rather than the top 5 methods. 101 110 2020/12/162019/08/152020/08/92019/09/282019/07/152019/07/262019/10/152019/09/27 1398/7/5 2021/05/222021/01/102021/01/102020/08/182020/02/32021/05/232021/12/62020/11/21 1399/9/1 محمد جعفرآباد mohammad jafarabad گروه مهندسی فناوری اطلاعات، دانشکده فنی و مهندسی، دانشگاه قم ایران tcsms@yahoo.com روح الله دیانت Rouhollah Dianat گروه مهندسی فناوری اطلاعات، دانشکده فنی و مهندسی، دانشگاه قم ایران rouhollahdianat@gmail.com Glove Word2vec Crowdsourcing word embedding classification جمع‌سپاری تعبیه لغات گلاو وردتووک طبقه‌بندی [1] H. Rheingold, Smart mobs: The next social revolution. Basic books, 2007.##[2] D. Zhou, Q. Liu, J. C. Platt, C. Meek, & N. B. Shah, Regularized minimax conditional entropy for crowdsourcing. arXiv preprint arXiv:1503.07240, 2015.##[3] Y. Zhao, & Q. Zhu, "Evaluation on crowdsourcing research: Current status and future direction", Information Systems Frontiers, vol. 16(3), pp. 417-434, 2014.##[4] S. Marjanovic, C. Fry, & J. Chataway, "Crowdsourcing based business models: In search of evidence for innovation 2.0", Science and public policy, vol. 39(3), pp. 318-332, 2012.##[5] J. Prpić, P. P.Shukla, J. H. Kietzmann, & I. P. McCarthy, "How to work a crowd: Developing crowd capital through crowdsourcing", Business Horizons, vol. 58(1), pp. 77-85, 2015.##[6] J. Staiano and M. Guerini, "DepecheMood: a Lexicon for emotion analysis from crowd-annotated news," arXiv preprint arXiv1405, pp. 1605, 2014.##[7] P. Gonçalves, M. Araújo, F. Benevenuto, and M. Cha, "Comparing and combining sentiment analysis methods," in Proceedings of the first ACM conference on Online social networks, 2013, pp. 27-38.##[8] P. Belleflamme, T. Lambert, & A. 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توصیف‌گر موضعی جدید با استفاده از نگاشت مرکاتور به‌منظور تشخیص اشیای سه‌بعدی A novel local feature descriptor using the Mercator projection for 3D object recognition 1 1 پردازش ابرهای نقطه‌ای یکی از زمینه‌های در حال رشد در بینایی ماشین است. با پیدایش حس‌گرهای عمق ارزان‌قیمت علاقه زیادی به پردازش ابرهای نقطه‌ای و استفاده از آن در تشخیص اشیای سه‌بعدی ایجاد شده است. در حالت کلی روش‌های تشخیص اشیای سه‌بعدی به دو دسته‌ موضعی و سرتاسری تقسیم می‌شوند. در روش‌های سرتاسری شکل کلی مدل توصیف‌شده درحالی‌که در روش‌های موضعی از خصوصیات هندسی ناحیه موضعی اطراف یک نقطه برای به‌دست‌آوردن ویژگی‌ آن نقطه استفاده می‌شود. برخلاف روش‌های سرتاسری، روش‌های موضعی نیاز به قطعه‌بندی ندارند و نسبت به پدیده انسداد و درهم‌ریختگی مقاوم‌تر هستند. روش‌های مبتنی بر ویژگی‌های موضعی، برخی از ویژگی‌های هندسی را از سطوح محلی اطراف نقاط خاصی به نام نقاط کلیدی استخراج می‌کنند. ویژگی‌های هندسی یک نقطه کلیدی در یک توصیف‌گر ویژگی کدگذاری می‌شوند. چگونگی توصیف محیط پیرامون یک نقطه کلیدی چالش اصلی این روش هاست. روش‌های موضعی که به‌طورمعمول مورد استفاده قرار می‌گیرند، اغلب به نوفه، تغییر وضوح مش و تبدیل صلب حساس هستند. برای غلبه بر چنین مشکلاتی، در این مقاله توصیف‌گر موضعی جدیدی بر اساس نگاشت مرکاتور ارائه شده ‌است. نگاشت مرکاتور یکی از معروف‌ترین نگاشت‌های سه بعد به دو بعد است که فاصله، زاویه، جهت، طول و عرض جغرافیایی نسبی را بین هر دو نقطه در ابرهای نقطه‌ای حفظ می‌کند. به‌منظور ارزیابی، روش پیشنهادی با تعدادی از روش‌های مطرح مقایسه شده‌ است. برتری این روش بر سایر روش‌ها با استفاده از معیارهای خطای جذر میانگین مربعات، نمودار بازخوانی در برابر دقت، خطای ثبت‌کردن، خطای چرخش و انتقال نشان داده می‌شود و اثبات می‌شود که این روش قدرت توصیفی خوبی دارد و نسبت به تبدیل صلب، نویز و تغییر وضوح مش مقاوم است. 2 The processing of point clouds is one of the growing areas in machine vision. With the advent of inexpensive depth sensors, there has been a great interest in point clouds to detect three-dimensional objects. In general, 3D object recognition methods are alienated into two classes: local and global feature-based methods.  In global feature-based methods, the entire shape of the model is described, while in local methods, the geometric properties of the local area around a point are used to obtain the characteristic of the point. Unlike global methods, local methods do not entail any segmentation and they are more robust to clutter and occlusion. The local feature-based methods extract some geometric features from local surfaces around specific points named keypoints. The geometric features of a keypoint are encoded into a feature descriptor. How to describe the environment around a keypoint is the main challenge of these methods. The commonly used local feature-based methods often are sensitive to noise, varying mesh resolution, and rigid transformation. To overcome such disadvantages, in this paper, a new local feature descriptor based on the Mercator projection is proposed. The Mercator projection is one of the most popular 3D to 2D projections that can preserve true distance, direction, and relative longitude and latitude between any two points in point clouds. To evaluate, the proposed method has been compared with several state-of-the-art descriptor methods. The superiority of this method over other methods is shown by using the criteria of square Root Mean Square Error (RMSE), Recall versus 1-Precision Curve (RPC), and registration correction, rotation, and translation errors, and it is proved that this method has good descriptiveness power and it is robust to noise and varying mesh resolution. Introduction In this paper, we propose a new local descriptor to provide robust and precise geometric features. The geometric features are extracted using the Mercator projection of the neighborhood sphere. Our contributions are as follows: (1) The proposed descriptor directly learns from the point clouds (2) using the proposed method, there is only one representation for each point so the problem of multiple representations of a point is addressed. Also, the Mercator projection has many properties that make it appropriate for data representations in a point cloud. (3) It can accurately describe the geometric properties around a point. (3) The Mercator projection is a conformal projection so it preserves true distances, directions, and relative longitudes and latitudes. (4) It keeps small element geometry, which means Mercator projection preserves the shapes of small regions.  The proposed method Given a query point p, a sphere of radius r is centered at p for determining the neighbor points. Then Mercator projection is used for mapping the sphere into a plane with considering the Local reference frame (LRF) as previously suggested by Tombaret al. (2010b). The Mercator projection is a cylindrical projection that was proposed by G. Mercator in 1569. In this projection, the surface of a sphere is mapped into a plane. It preserves true distances, directions, and relative longitudes and latitudes. The Mercator projection for each point is identified using two following equations: (2) where λ is the  longitude and φ is the  latitude of a point  in the sphere, and (x,  y) represents corresponding point  in the Cartesian map. For extracting images as the input of the Siamese network, we need ranges for achieved x and  y. The variable x is in the interval [−π,  π] but range of y is different for the Mercator projection of each keypoint. As a result, the minimum and maximum of the variable y for all neighbor points are considered as the range of y, then a histogram 30 × 30 is measured. The Mercator projections of all neighbors are defined and the number of points  in each bin counted. Then we normalize the histogram by dividing each bin by the total number of neighbor points, it causes more robustness to noise and mesh resolution. Results and discussion The performance of the proposed method is evaluated on the Bologna (Tombari et al., 2010c) and John Burkardt in terms of RMSE, RPC and registration correction rate, rotation and translation errors. 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ارائه یک روش مؤثر برای یادگیری مقاوم متریک در برابر نوفه برچسب An Effective Approach for Robust Metric Learning in the Presence of Label Noise 1 1 تعیین شباهت/ فاصله داده‌ها در بسیاری از الگوریتم‌های یادگیری ماشین، شناسایی الگو و داده‌کاوی کاربرد دارد. در بسیاری از کاربردها، معیارهای عمومی شباهت/فاصله کارایی بالایی ندارد و به‌طورمعمول با استفاده از داده‌ها می‌توان معیار مناسب‌تری را یاد گرفت. داده‌های آموزشی برای این منظور به‌طورمعمول به‌صورت زوج‌های مشابه و نامشابه و یا محدودیت‌های سه‌گانه هستند. در کاربردهای واقعی، این داده‌های آموزشی از طریق اینترنت و به‌طورمعمول با روش‌هایی نظیر Crowdsourcing جمع‌آوری می‌شود که می‌تواند حاوی نوفه و اطلاعات اشتباه باشد. کارایی روش‌های یادگیری متریک در صورت وجود اطلاعات آموزشی نوفه‌ای و اشتباه به‌شدت افت می‌کند و حتی ممکن است این روش‌ها از معیارهای عمومی فاصله نظیر اقلیدسی نیز بدتر عمل کنند. بنابراین نیاز به مقاوم‌سازی روش‌های یادگیری متریک در برابر نوفه برچسب وجود دارد. در این پژوهش، یک تابع احتمالاتی جدید برای تعیین احتمال نوفه‌ای‌‌بودن برچسب داده‌ها با استفاده از محدودیت‌های سه‌گانه آموزشی ارائه‌شده است که باعث می‌شود، الگوریتم یادگیری متریک بتواند داده‌های پرت و نوفه‌ای را شناسایی کند و تأثیر آن‌ها را فرایند یادگیری کاهش دهد. همچنین نشان داده‌ شده است که چگونه از اطلاعات به‌دست‌آمده می‌توان برای افزایش کارایی الگوریتم مبتنی بر متریک (مانند kNN) بهره برد و عملکرد آن را به‌طور قابل‌ملاحظه‌ای افزایش داد. نتایج آزمایش‌ها بر روی مجموعه‌ای از داده­‌های ساختگی و واقعی، تأیید می‌کند که روش پیشنهادی به‌طور قابل‌ملاحظه‌ای کارایی روش‌های یادگیری متریک را در محیط‌هایی با نوفه برچسب بهبود می‌بخشد و بر روش‌های همتا در مرزهای دانش در سطوح مختلف نوفه برچسب برتری دارد. 2 Many algorithms in machine learning, pattern recognition, and data mining are based on a similarity/distance measure. For instance, the kNN classifier and clustering algorithms such as k-means require a similarity/distance function. Also, in Content-Based Information Retrieval (CBIR) systems, we need to rank the retrieved objects based on the similarity to the query. As generic measures like Euclidean and cosine similarity are not appropriate in many applications, metric learning algorithms have been developed with the aim of learning an optimal distance function from data. These methods often need training data in the form of pair or triplet sets. Nowadays, this training data is popularly obtained via crowdsourcing from the Internet.  Therefore, this information may be contaminated with label noise resulting in the poor performance of the learned metric. In some datasets, even it is possible that the learned metrics perform worse than the general ones such as Euclidean. To address this emerging challenge, we present a new robust metric learning algorithm that can identify outliers and label noise simultaneously from training side information. For this purpose, we model the probability distribution of label noise based on information in the training data. The proposed distribution function efficiently assigns the high probability to the data points contaminated with label noise. On the other hand, its value on the normal instances is near zero. Afterward, we weight the training instances according to these probabilities in our metric learning optimization problem. The proposed optimization problem can be solved using available SVM libraries such as LibSVM efficiently. Note that the proposed approach for identifying data with label noise is general and can easily be applied to any existing metric learning algorithms. After the metric learning phase, we utilized both the weights and the learned metric to enhance the accuracy of the metric-based classifier such as kNN. Several experiments are conducted on both real and synthetic datasets. The results confirm that the proposed algorithm enhances the performance of the learned metric in the presence of label noise and considerably outperforms state-of-the-art peer methods at different noise levels. 125 136 2020/12/162019/08/152020/08/92019/09/282019/07/152019/07/262019/10/152019/09/272020/08/92019/12/5 1398/9/14 2021/05/222021/01/102021/01/102020/08/182020/02/32021/05/232021/12/62020/11/212022/01/152020/08/18 1399/5/28 داود ذبیح زاده Davood Zabihzadeh دانشگاه حکیم سبزواری ایران d.zabihzadeh@gmail.com سعید زاهدی Saeed Zahedi دانشگاه فردوسی مشهد ایران s.zahedi@mail.um.ac.ir رضا منصفی Reza Monsefi دانشگاه فردوسی مشهد ایران monsefi@um.ac.ir Robust Metric Learning Label Noise Outlier Distance Measure یادگیری متریک مقاوم نوفه برچسب داده‌های پرت معیار فاصله [1] D. Zabihzadeh, R. Monsefi, and H. S. Yazdi, "Sparse Bayesian similarity learning based on posterior distribution of data," Engineering Applications of Artificial Intelligence, vol. 67, pp. 173-186, 2018.##[2] L. Lin, G. Wang, W. Zuo, X. Feng, and L. Zhang, "Cross-domain visual matching via generalized similarity measure and feature learning," IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 6, pp. 1089-1102, 2017.##[3] J. Lu, X. Zhou, Y.-P. Tan, Y. Shang, and J. Zhou, "Neighborhood repulsed metric learning for kinship verification," IEEE transactions on pattern analysis and machine intelligence, vol. 36, no. 2, pp. 331-345, 2014.##[4] S. Bak and P. Carr, "One-Shot Metric Learning for Person Re-identification," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2990-2999.##[5] N. Jiang, W. Liu, and Y. Wu, "Order determination and sparsity-regularized metric learning adaptive visual tracking," in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, 2012: IEEE, pp. 1956-1963.##[6] M. Guillaumin, T. Mensink, J. Verbeek, and C. Schmid, "Tagprop: Discriminative metric learning in nearest neighbor models for image auto-annotation," in Computer Vision, 2009 IEEE 12th International Conference on, 2009: IEEE, pp. 309-316.##[7] G. Chechik, V. Sharma, U. Shalit, and S. Bengio, "Large Scale Online Learning of Image Similarity Through Ranking," J. Mach. Learn. Res., vol. 11, pp. 1109-1135, 2010.##[8] X. Hao, S. C. H. Hoi, J. Rong, and Z. Peilin, "Online Multiple Kernel Similarity Learning for Visual Search," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 36, no. 3, pp. 536-549, 2014, doi: 10.1109/TPAMI-.2013.149.##[9] P. Wu, S. C. H. Hoi, P. Zhao, C. Miao, and Z. Y. Liu, "Online Multi-Modal Distance Metric Learning with Application to Image Retrieval," IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 2, pp. 454-467, 2016, doi: 10.1109/TKDE.2015.2477296.##[10] J. Li, C. Xu, W. Yang, C. Sun, and D. Tao, "Discriminative Multi-View Interactive Image Re-Ranking," IEEE Transactions on Image Processing, 2017.##[11] A. Bellet, A. Habrard, and M. Sebban, "A Survey on Metric Learning for Feature Vectors and Structured Data," Technical report, 2014.##[12] B. Frénay and M. Verleysen, "Classification in the presence of label noise: a survey," IEEE transactions on neural networks and learning systems, vol. 25, no. 5, pp. 845-869, 2013.##[13] T. Yang, R. Jin, and A. K. Jain, "Learning from noisy side information by generalized maximum entropy model," in Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010: Citeseer, pp. 1199-1206.##[14] K. Huang, R. Jin, Z. Xu, and C.-L. Liu, "Robust metric learning by smooth optimiza-tion," arXiv preprint arXiv:1203.3461, 2012.##[15] Y. Nesterov, "Smooth minimization of non-smooth functions," Mathematical programm-ing, vol. 103, no. 1, pp. 127-152, 2005.##[16] D. Wang and X. Tan, "Robust Distance Metric Learning in the Presence of Label Noise," in AAAI, 2014, pp. 1321-1327.##[17] H. Wang, F. Nie, and H. Huang, "Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and Maximization," in Proceedings of the 31st International Conference on Machine Learning (ICML-14), T. Jebara and E. P. Xing, Eds., 2014, [Formatter not found: ResolvePDF]: JMLR Workshop and Conference Proceedings, pp. 1836-1844. [Online]. Available: http://jml-r.org/proceedings/papers/v32/wangj14.pdf. [Online]. Available: http://jmlr.org/proceed-ings/papers/v32/wangj14.pdf##[18] S. Xiang, F. Nie, and C. Zhang, "Learning a Mahalanobis distance metric for data clustering and classification," Pattern Recogn., vol. 41, no. 12, pp. 3600-3612, 2008, doi: 10.1016/j.patcog.2008.05.018.##[19] D. Wang and X. Tan, "Robust Distance Metric Learning via Bayesian Inference," IEEE Transactions on Image Processing, vol. 27, no. 3, pp. 1542-1553, 2018.##[20] D. Zabihzadeh, R. Monsefi, and H. S. Yazdi, "Sparse Bayesian approach for metric learning in latent space," Knowledge-Based Systems, vol. 178, pp. 11-24, 2019.##[21] K. Q. Weinberger and L. K. Saul, "Distance Metric Learning for Large Margin Nearest Neighbor Classification," J. Mach. Learn. Res., vol. 10, pp. 207-244, 2009.##[22] S. Al-Obaidi, D. Zabihzadeh, A. S. Rasheed, and R. Monsefi, "Robust Metric Learning based on the Rescaled Hinge Loss," arXiv preprint arXiv:1904.11711, 2019.##[23] F. Wang, W. Zuo, L. Zhang, D. Meng, and D. Zhang, "A kernel classification framework for metric learning," IEEE transactions on neural networks and learning systems, vol. 26, no. 9, pp. 1950-1962, 2015.##[24] C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM transactions on intelligent systems and technology (TIST), vol. 2, no. 3, p. 27, 2011.##[25] L. v. d. Maaten and G. Hinton, "Visualizing data using t-SNE," Journal of machine learning research, vol. 9, no. Nov, pp. 2579-2605, 2008.##[1] D. Zabihzadeh, R. Monsefi, and H. S. Yazdi, "Sparse Bayesian similarity learning based on posterior distribution of data," Engineering Applications of Artificial Intelligence, vol. 67, pp. 173-186, 2018.##[2] L. Lin, G. Wang, W. Zuo, X. Feng, and L. Zhang, "Cross-domain visual matching via generalized similarity measure and feature learning," IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 6, pp. 1089-1102, 2017.##[3] J. Lu, X. Zhou, Y.-P. Tan, Y. Shang, and J. Zhou, "Neighborhood repulsed metric learning for kinship verification," IEEE transactions on pattern analysis and machine intelligence, vol. 36, no. 2, pp. 331-345, 2014.##[4] S. Bak and P. Carr, "One-Shot Metric Learning for Person Re-identification," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 2990-2999.##[5] N. Jiang, W. Liu, and Y. Wu, "Order determination and sparsity-regularized metric learning adaptive visual tracking," in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, 2012: IEEE, pp. 1956-1963.##[6] M. Guillaumin, T. Mensink, J. Verbeek, and C. Schmid, "Tagprop: Discriminative metric learning in nearest neighbor models for image auto-annotation," in Computer Vision, 2009 IEEE 12th International Conference on, 2009: IEEE, pp. 309-316.##[7] G. Chechik, V. Sharma, U. Shalit, and S. Bengio, "Large Scale Online Learning of Image Similarity Through Ranking," J. Mach. Learn. Res., vol. 11, pp. 1109-1135, 2010.##[8] X. Hao, S. C. H. Hoi, J. Rong, and Z. Peilin, "Online Multiple Kernel Similarity Learning for Visual Search," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 36, no. 3, pp. 536-549, 2014, doi: 10.1109/TPAMI-.2013.149.##[9] P. Wu, S. C. H. Hoi, P. Zhao, C. Miao, and Z. Y. Liu, "Online Multi-Modal Distance Metric Learning with Application to Image Retrieval," IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 2, pp. 454-467, 2016, doi: 10.1109/TKDE.2015.2477296.##[10] J. Li, C. Xu, W. Yang, C. Sun, and D. Tao, "Discriminative Multi-View Interactive Image Re-Ranking," IEEE Transactions on Image Processing, 2017.##[11] A. Bellet, A. Habrard, and M. Sebban, "A Survey on Metric Learning for Feature Vectors and Structured Data," Technical report, 2014.##[12] B. Frénay and M. Verleysen, "Classification in the presence of label noise: a survey," IEEE transactions on neural networks and learning systems, vol. 25, no. 5, pp. 845-869, 2013.##[13] T. Yang, R. Jin, and A. K. Jain, "Learning from noisy side information by generalized maximum entropy model," in Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010: Citeseer, pp. 1199-1206.##[14] K. Huang, R. Jin, Z. Xu, and C.-L. Liu, "Robust metric learning by smooth optimiza-tion," arXiv preprint arXiv:1203.3461, 2012.##[15] Y. Nesterov, "Smooth minimization of non-smooth functions," Mathematical programm-ing, vol. 103, no. 1, pp. 127-152, 2005.##[16] D. Wang and X. Tan, "Robust Distance Metric Learning in the Presence of Label Noise," in AAAI, 2014, pp. 1321-1327.##[17] H. Wang, F. Nie, and H. Huang, "Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and Maximization," in Proceedings of the 31st International Conference on Machine Learning (ICML-14), T. Jebara and E. P. Xing, Eds., 2014, [Formatter not found: ResolvePDF]: JMLR Workshop and Conference Proceedings, pp. 1836-1844. [Online]. Available: http://jml-r.org/proceedings/papers/v32/wangj14.pdf. [Online]. Available: http://jmlr.org/proceed-ings/papers/v32/wangj14.pdf##[18] S. Xiang, F. Nie, and C. Zhang, "Learning a Mahalanobis distance metric for data clustering and classification," Pattern Recogn., vol. 41, no. 12, pp. 3600-3612, 2008, doi: 10.1016/j.patcog.2008.05.018.##[19] D. Wang and X. Tan, "Robust Distance Metric Learning via Bayesian Inference," IEEE Transactions on Image Processing, vol. 27, no. 3, pp. 1542-1553, 2018.##[20] D. Zabihzadeh, R. Monsefi, and H. S. Yazdi, "Sparse Bayesian approach for metric learning in latent space," Knowledge-Based Systems, vol. 178, pp. 11-24, 2019.##[21] K. Q. Weinberger and L. K. Saul, "Distance Metric Learning for Large Margin Nearest Neighbor Classification," J. Mach. Learn. Res., vol. 10, pp. 207-244, 2009.##[22] S. Al-Obaidi, D. Zabihzadeh, A. S. Rasheed, and R. Monsefi, "Robust Metric Learning based on the Rescaled Hinge Loss," arXiv preprint arXiv:1904.11711, 2019.##[23] F. Wang, W. Zuo, L. Zhang, D. Meng, and D. Zhang, "A kernel classification framework for metric learning," IEEE transactions on neural networks and learning systems, vol. 26, no. 9, pp. 1950-1962, 2015.##[24] C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM transactions on intelligent systems and technology (TIST), vol. 2, no. 3, p. 27, 2011.##[25] L. v. d. Maaten and G. Hinton, "Visualizing data using t-SNE," Journal of machine learning research, vol. 9, no. Nov, pp. 2579-2605, 2008. ##

توسعه روش‌های مبتنی بر رفع نویز اسپکل تصویر جهت رفع نویز ویدئو ویسار Extending SAR Image Despckling methods for ViSAR Denoising 1 1 رادارهای سار (SAR) یکی از ابزارهای تصویربرداری در شرایط مختلف آب‌ و هوایی در کاربردهای نقشه‌برداری، نظامی، منابع زمینی و عمرانی می‌باشند. در سال‌های اخیر یک رادار جدید، جهت ثبت ویدئو اشیا در حالت حرکت با توسعه رادارهای سار به نام ویدئوسار یا به‌اختصار ویسار (ViSAR) برای نظارت محیطی ارائه‌شده است. به مانند تصاویر سار، یکی از چالش‌های اساسی در داده ویسار نیز وجود نویز اسپکل است. در این مقاله با سفارشی‌سازی روش‌های رفع نویز تصویر برای رفع نویز ویدئو ویسار، سه رویکرد مختلف شامل "فریم به فریم"، "میانگین‌گیری" و "سه بعدی" ارائه و ارزیابی شده است. در رویکرد نخست، بدون توجه به بُعد زمان هر فریم از ویدئو به‌صورت مجزا رفع نویز شده و در رویکرد دوم، از میانگین‌گیری فریم‌های رفع‌ نویزشده در بُعد زمان استفاده شده‌است. در رویکرد سه بعدی، از بلاک‌های سه‌بعدی در بُعد مکان و زمان برای رفع نویز در ویدئو استفاده شده است. علاوه بر این رویکردها، راهکار جدیدی با نام ViSAR Incremental BM3D یا به اختصار ViSAR-IBM3D با توسعه روش مشهور رفع نویز تصویر SAR-BM3D نیز ارائه شده که توانسته است با تغیر ساختار این روش برای ویدئو، زمان اجرای کمتر و حفظ جزئیات بهتری را به همراه آورد. روش SAR-BM3D در گام نخست در فضای موجک تخمین اولیه از تصویر بدون نویز را محاسبه کرده و سپس در گام دوم به‌کمک تصویر نویزی و تخمین اولیه، تصویر رفع نویز شده نهایی را تخمین می‌زند. در روش ViSAR-IBM3D با بهره‌گیری از همبستگی بین فریم‌های متوالی ویدئو، از نتیجه فریم قبلی برای رفع نویز فریم جاری استفاده شده تا بتوان علاوه بر حفظ جزئیات و تمایز تصاویر، زمان اجرای الگوریتم را نیز بهبود بخشید. نتایج به‌دست‌آمده بر روی ویدئو با نویز شبیه‌سازی و همچنین ویدئو واقعی ویسار، کارایی رویکرد سه‌بعدی پیشنهادی نسبت با سایر رویکردها و همچنین کارایی بالاتر روش پیشنهادی ViSAR-IBM3D نسبت به روش‌های قبلی را نشان می‌دهد.   2 Synthetic Aperture Radar (SAR) is widely used in different weather conditions for various applications such as mapping, remote sensing, urban, civil, and military monitoring. Recently, a new radar sensor called Video SAR (ViSAR) has been developed to capture sequential frames from moving objects for environmental monitoring applications such as image or video segmentation, classification and change detection. Same as SAR images, the major problem of ViSAR is the presence of speckle noise. In this paper, the performance of several image-based denoising methods is studied for de-speckling of ViSAR frames through “Frame-by-Frame”, “Averaging” and “3D” schemes. In “Frame-by-Frame” scheme, each video frame is denoised independently of the other frames; whereas, in “Averaging” scheme, the denoised images are averaged along a time window. In “3D” scheme, denoising is performed on 3D blocks in space-time (x-y-t) domain. In addition to these schemes, a novel extension on SAR-BM3D method, called ViSAR Incremental BM3D (ViSAR-IBM3D) approach is proposed for video denoising. The SAR-BM3D method performs denoising in two steps. At the first step, it uses wavelet denoising to primitively denoise the original image; in the next step, this image in combination with the original image are used to estimate the final denoised image. The main challenge of SAR-BM3D method is high time complexity especially for video frames. Here, in ViSAR-IBM3D, we benefit from the correlation between the frames of video and utilize the denoised images in previous frame to de-speckle the current frame. The proposed method can remarkably reduce the time complexity and improve preserving the details and the contrast of the denoised frames. The experimental results evaluated on real-world ViSAR video as well as video with simulated noises show that the proposed 3D filtering scheme and the proposed ViSAR-IBM3D method achieve better denoising performance than the other ones. 137 152 2020/12/162019/08/152020/08/92019/09/282019/07/152019/07/262019/10/152019/09/272020/08/92019/12/52019/07/4 1398/4/13 2021/05/222021/01/102021/01/102020/08/182020/02/32021/05/232021/12/62020/11/212022/01/152020/08/182021/06/26 1400/4/5 زهرا عابدی Zahra Abedi دانشگاه یزد ایران zahra.abedi@stu.yazd.ac.ir مهدی یزدیان دهکردی Mahdi Yazdian-Dehkordi دانشگاه یزد ایران yazdian@yazd.ac.ir SAR ViSAR Noise Speckle SAR-BM3D ViSAR-IBM3D رفع نویز اسپکل سار ویدئو ویسار SAR-BM3D ViSAR-IBM3D [1] "Pathfinder Airborne ISR Systems: What Is Synthetic Aperture Radar?," [Online]. Available: http://www.sandia.gov/radar/what_is_sar/index.html. [Accessed 1 July 2017].##[2] H. Yan, X. Mao, J. Zhang and D. Zhu, "Frame rate analysis of video synthetic aperture radar (ViSAR)," in International Symposium on Antennas and Propagation (ISAP), 2016.##[3] X. Song and W. Yu, "Processing video-SAR data with the fast backprojection method," IEEE Transactions on Aerospace and Electronic Systems, vol. 52, no. 6, pp. 2838--2848, 2016.##[4] T. Yamaoka, K. Suwa, T. Hara and Y. Nakano, "Radar video generated from synthetic aperture radar image," in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016.##[5] A. Voisin, V. A. Krylov, G. Moser, S. B. Serpico and J. Zerubia, "Classification of very high resolution SAR images of urban areas using copulas and texture in a hierarchical Markov random field model," IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 1, pp. 96-100, 2013.##[6] F. Baselice, G. Ferraioli and V. Pascazio, "Markovian change detection of urban areas using very high resolution complex SAR images," IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 5, pp. 995-999, 2014.##[7] M. Pham, G. Mercier and J. Michel, "Change Detection Between SAR Images Using a Pointwise Approach and Graph Theory," IEEE Trans. Geoscience and Remote Sensing, vol. 54, no. 4, pp. 2020--2032, 2016.##[8] J. S. Lim, "Two-dimensional signal and image processing," Englewood Cliffs, NJ, Prentice Hall, 1990.##[9] V. S. Frost, J. A. Stiles, K. S. Shanmugan and J. C. Holtzman, "A model for radar images and its application to adaptive digital filtering of multiplicative noise," IEEE Transactions on pattern analysis and machine intelligence, vol. 4, no. 2, pp. 157-166, 1982.##[10] J.-S. Lee, "Digital image enhancement and noise filtering by use of local statistics," IEEE transactions on pattern analysis and machine intelligence, vol. 2, no. 2, pp. 165-168, 1980.##[11] D. T. Kuan, A. A. Sawchuk, T. C. Strand and P. Chavel, "Adaptive noise smoothing filter for images with signal-dependent noise," IEEE transactions on pattern analysis and machine intelligence, vol. 7, no. 2, pp. 165-177, 1985.##[12] S. Liu, M. Liu, P. Li, J. Zhao, Z. Zhu and X. Wang, "SAR Image Denoising via Sparse Representation in Shearlet Domain Based on Continuous Cycle Spinning," IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 5, pp. 2985-2992, 2017.##[13] S. Parrilli, M. Poderico, C. V. Angelino and L. Verdoliva, "A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage," IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 2, pp. 606-616, 2012.##[14] I. W. Selesnick, R. G. Baraniuk and K. N. G., "The Dual-Tree Complex Wavelet Transform," IEEE Signal Processing Magazine, vol. 22, no. 6, pp. 123-151, 2005.##[15] M. Mastriani and A. E. Giraldez, "Kalman-s Shrinkage for Wavelet-Based Despeckling of SAR Images," World Academy of ScienceEngineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineerin, vol. 2, no. 4, pp. 1213-1219, 2008.##[16] M. Nasri and H. Nezamabadi-pour, "Image denoising in the wavelet domain using a new adaptive thresholding function," Neurocomputing, vol. 72, no. 4, pp. 1012-1025, 2009.##[17] X.-P. Zhang, "Space-scale adaptive noise reduction in images based on thresholding neural network," in IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings, 2001.##[18] L. Xu, J. Li, Y. Shu and J. Peng, "SAR image denoising via clustering-based principal component analysis," IEEE transactions on geoscience and remote sensing, vol. 52, no. 11, pp. 6858-6869, 2014.##[19] B. Xu, Y. Cui, Z. Li, B. Zuo, J. Yang and J. Song, "Patch ordering-based SAR image despeckling via transform-domain filtering," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 4, pp. 1682-1695, 2014.##[20] B. Kanoun, G. Ferraioli, V. Pascazio and G. Schirinzi, "Fast gpu-based enhanced wiener filter for despeckling sar data," Remote Sensing, vol. 11, no. 12, pp. 1473, 2019.##[21] K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, "Image denoising by sparse 3-D transform-domain collaborative filtering," IEEE Transactions on image processing, vol. 16, no. 8, pp. 2080--2095, 2007.##[22] G. Chierchia, M. El Gheche, G. Scarpa and L. Verdoliva, "Multitemporal SAR Image Despeckling Based on Block-Matching and Collaborative Filtering," IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 10, pp. 5467-5480, 2017.##[23] Z. Abedi, M. Yazdian-Dehkordi, "Evaluation of Image Denoising Methods for ViSAR Data", in 23th National CSI Computer Conference, Tehran, Iran, 2018 (In Persian).##[24] C. P. Loizou, C. S. Pattichis, C. I. Christodoulou, R. S. Istepanian, M. Pantziaris and A. Nicolaides, "Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery," IEEE transactions on ultrasonics, ferroelectrics, and frequency control, vol. 52, no. 10, pp. 1653-1669, 2005.##[25] P. Courmontagne, "Speckle noise reduction: a review Advances in Seafloor-Mapping Sonar," 1 December 2007. [Online]. Available: http://departements.imt-atlantique.fr/data/iti/seafloor/presentations/ISEN_Courmontagne_Speckle_noise_reduction.pdf. [Accessed 1 December 2018].##[26] H. Xie, L. E. Pierce and F. T. Ulaby, "Statistical properties of logarithmically transformed speckle," IEEE Transactions on Geoscience and Remote Sensing, vol. 40, no. 3, pp. 721-727, 2002.##[27] T. Shu, D. Xie, B. Rothrock, S. Todorovic and S. Chun Zhu, "Joint inference of groups, events and human roles in aerial videos," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.##[28] A. Bhattacharya, "Centre of Studies in Resources Engineering," 11 April 2013. [Online]. Available: http://www.csre.iitb.ac.i-n/~avikb/GNR647/Lec_11_Speckle.pdf. [Accessed 20 February 2019].##[29] H. Xie, L. E. Pierce and F. T. Ulaby, "Statistical Properties of Logarithmically Transformed Speckle," IEEE Transactions on Geoscience and Remote Sensing, vol. 40, no. 3, pp. 721-727, 2002.##[30] Z. {Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, "Image quality assessment: from error visibility to structural similarity," IEEE transactions on image processing, vol. 13, no. 4, pp. 600-612, 2004.##[31] X. Wang, L. Ge and X. Li, "Evaluation of Filters for Envisat Asar Speckle Suppression in Pasture Area," ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 341-346, 2012.##[32] H. R. {Sheikh and A. C. Bovik, "Image information and visual quality," IEEE Transactions on image processing, vol. 15, no. 2, pp. 430-444, 2006.##[1] "Pathfinder Airborne ISR Systems: What Is Synthetic Aperture Radar?," [Online]. Available: http://www.sandia.gov/radar/what_is_sar/index.html. [Accessed 1 July 2017].##[2] H. Yan, X. Mao, J. Zhang and D. Zhu, "Frame rate analysis of video synthetic aperture radar (ViSAR)," in International Symposium on Antennas and Propagation (ISAP), 2016.##[3] X. Song and W. Yu, "Processing video-SAR data with the fast backprojection method," IEEE Transactions on Aerospace and Electronic Systems, vol. 52, no. 6, pp. 2838--2848, 2016.##[4] T. Yamaoka, K. Suwa, T. Hara and Y. Nakano, "Radar video generated from synthetic aperture radar image," in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016.##[5] A. Voisin, V. A. Krylov, G. Moser, S. B. Serpico and J. Zerubia, "Classification of very high resolution SAR images of urban areas using copulas and texture in a hierarchical Markov random field model," IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 1, pp. 96-100, 2013.##[6] F. Baselice, G. Ferraioli and V. Pascazio, "Markovian change detection of urban areas using very high resolution complex SAR images," IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 5, pp. 995-999, 2014.##[7] M. Pham, G. Mercier and J. Michel, "Change Detection Between SAR Images Using a Pointwise Approach and Graph Theory," IEEE Trans. Geoscience and Remote Sensing, vol. 54, no. 4, pp. 2020--2032, 2016.##[8] J. S. Lim, "Two-dimensional signal and image processing," Englewood Cliffs, NJ, Prentice Hall, 1990.##[9] V. S. Frost, J. A. Stiles, K. S. Shanmugan and J. C. Holtzman, "A model for radar images and its application to adaptive digital filtering of multiplicative noise," IEEE Transactions on pattern analysis and machine intelligence, vol. 4, no. 2, pp. 157-166, 1982.##[10] J.-S. Lee, "Digital image enhancement and noise filtering by use of local statistics," IEEE transactions on pattern analysis and machine intelligence, vol. 2, no. 2, pp. 165-168, 1980.##[11] D. T. Kuan, A. A. Sawchuk, T. C. Strand and P. Chavel, "Adaptive noise smoothing filter for images with signal-dependent noise," IEEE transactions on pattern analysis and machine intelligence, vol. 7, no. 2, pp. 165-177, 1985.##[12] S. Liu, M. Liu, P. Li, J. Zhao, Z. Zhu and X. Wang, "SAR Image Denoising via Sparse Representation in Shearlet Domain Based on Continuous Cycle Spinning," IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 5, pp. 2985-2992, 2017.##[13] S. Parrilli, M. Poderico, C. V. Angelino and L. Verdoliva, "A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage," IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 2, pp. 606-616, 2012.##[14] I. W. Selesnick, R. G. Baraniuk and K. N. G., "The Dual-Tree Complex Wavelet Transform," IEEE Signal Processing Magazine, vol. 22, no. 6, pp. 123-151, 2005.##[15] M. Mastriani and A. E. Giraldez, "Kalman-s Shrinkage for Wavelet-Based Despeckling of SAR Images," World Academy of ScienceEngineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineerin, vol. 2, no. 4, pp. 1213-1219, 2008.##[16] M. Nasri and H. Nezamabadi-pour, "Image denoising in the wavelet domain using a new adaptive thresholding function," Neurocomputing, vol. 72, no. 4, pp. 1012-1025, 2009.##[17] X.-P. Zhang, "Space-scale adaptive noise reduction in images based on thresholding neural network," in IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings, 2001.##[18] L. Xu, J. Li, Y. Shu and J. Peng, "SAR image denoising via clustering-based principal component analysis," IEEE transactions on geoscience and remote sensing, vol. 52, no. 11, pp. 6858-6869, 2014.##[19] B. Xu, Y. Cui, Z. Li, B. Zuo, J. Yang and J. Song, "Patch ordering-based SAR image despeckling via transform-domain filtering," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 4, pp. 1682-1695, 2014.##[20] B. Kanoun, G. Ferraioli, V. Pascazio and G. Schirinzi, "Fast gpu-based enhanced wiener filter for despeckling sar data," Remote Sensing, vol. 11, no. 12, pp. 1473, 2019.##[21] K. Dabov, A. Foi, V. Katkovnik and K. Egiazarian, "Image denoising by sparse 3-D transform-domain collaborative filtering," IEEE Transactions on image processing, vol. 16, no. 8, pp. 2080--2095, 2007.##[22] G. Chierchia, M. El Gheche, G. Scarpa and L. Verdoliva, "Multitemporal SAR Image Despeckling Based on Block-Matching and Collaborative Filtering," IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 10, pp. 5467-5480, 2017.##[23] Z. Abedi, M. Yazdian-Dehkordi, "Evaluation of Image Denoising Methods for ViSAR Data", in 23th National CSI Computer Conference, Tehran, Iran, 2018 (In Persian).##[23] ز. عابدی و م. یزدیان دهکردی, "ارزیابی روش‌های کاهش نویز اسپکل برای داده‌های ویدئویی ViSAR," در بیست و سومین کنفرانس ملی سالانه انجمن کامپیوتر ایران, تهران, 1396.##[24] C. P. Loizou, C. S. Pattichis, C. I. Christodoulou, R. S. Istepanian, M. Pantziaris and A. Nicolaides, "Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery," IEEE transactions on ultrasonics, ferroelectrics, and frequency control, vol. 52, no. 10, pp. 1653-1669, 2005.##[25] P. Courmontagne, "Speckle noise reduction: a review Advances in Seafloor-Mapping Sonar," 1 December 2007. [Online]. 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وارسی ویژگی دسترس‌پذیری در سامانه‌های نرم‌افزاری پیچیده و هم‌روند با استفاده از الگوریتم‌های جستجوی هوشمند Reachability checking in complex and concurrent software systems using intelligent search methods 1 1 روش وارسی مدل، روشی رسمی و مؤثر جهت تأیید سامانه‌های نرم‌افزاری است که با تولید و بررسی همه حالت­هایِ ممکنِ مدلی از سامانه نرم‌افزار به تحلیل آن می­‌پردازد. در سامانه‌های ایمنی– بحرانی، نمی­توان ریسک بروز خطا را حتی در فرآیند تست پذیرفت و لذا لازم است فرآیند درستی‌یابی، قبل از پیاده‌­سازی و در سطح مدل انجام شود. استفاده از این روش به‌منظور بررسی خواصی مانند ایمنی ایجاب می‌­کند که تمام حالت‌­های قابل دسترس (تمام فضای حالت) تولید و سپس فضای حالت سامانه مورد نظر به‌صورت دقیق بررسی شوند. چالش اساسی روش وارسی مدل در سامانه‌های بزرگ و پیچیده که دارای فضای حالت گسترده و نامحدود هستند، مشکل انفجار فضای حالت (کمبود حافظه در تولید همه حالت‌های ممکن) است. سامانه‌های تبدیل گراف، از پرکاربردترین سامانه‌های مدل‌سازی رسمی و راه‌کاری مناسب به‌منظور مدل‌­سازی و وارسی سامانه‌های پیچیده هستند. در سامانه‌هایی که تأیید ویژگی ایمنی غیرممکن است، می­‌توان با جستجویِ یک حالت قابل دسترسی که در آن پیکربندی خاصی (به‌عنوان مثال خطا یا رفتار نامطلوب) رخ می­‌دهد، ویژگی ایمنی را رد کرد. مطالعات اخیر حاکی از آن است که اکتشاف جزئی و هوشمندانه بخشی از فضای حالت می‌­تواند راه حل مناسبی برای مشکل انفجار فضای حالت باشد. هدف این پژوهش، استفاده از الگوریتم جنگل تصادفی در وارسی مدل است که می‌تواند با گزینش تعداد محدودی مسیر امیدبخش مشکل انفجار فضای حالت را برطرف سازد. مسیری امیدبخش است که احتمال رسیدن به یک جواب از طریق این مسیر، بیشتر از بقیه مسیرها باشد. در روش پیشنهادی، ابتدا مدل کوچکی از سامانه با استفاده از زبان رسمی سامانه توصیف گراف (GTS) ایجاد، سپس، از فضای حالت مدل کوچک، مجموعه‌ آموزشی از مسیرهایی که به هدف می‌رسند ایجاد می‌شود. پس‌ازآن، مجموعه آموزشی تولیدشده در اختیار الگوریتم جنگل تصادفی قرار داده می‌شود تا روابط منطقی موجود در آن شناسایی و کشف شوند. در مرحله بعد از دانش به‌دست‌آمده جهت پیمایش هوشمند و غیر کامل فضایِ حالتِ مدلِ بزرگ استفاده می‌شود. رویکرد پیشنهادی برای تأیید ویژگی دسترس‌پذیری و رد ویژگی ایمنی در سامانه‌های بزرگ و پیچیده که ایجاد تمام فضای حالت سامانه ناممکن است، استفاده می‌­شود. به منظور ارزیابی رویکرد پیشنهادی، این رویکرد  در ابزار GROOVEکه از ابزار متن‌باز برای طراحی و وارسی مدل برای سامانه‌های تبدیل گراف است، اجراشده است. نتایج نشان می‌دهند که روش پیشنهادی ازنظر میانگین زمان اجرا و طول شاهد تولیدشده نسبت به روش‌های مورد مقایسه عملکرد بهتری دارد. 2 The model checking technique is a formal and effective method for verifying software systems, which analyses it via generating and examining all possible states of a model of the software system. In safety-critical systems, one could not admit the risk of error even in the testing process, therefore it is necessary to carry out the verification process before implementation and at the model level. Using this technique to evaluate properties such as security entails all available states (all state space) being generated, then the state space of the system in question be carefully examined. The main challenge of the model checking technique in large and complex systems with wide or infinite state space is the problem of state space explosion (lack of memory in the generation of all possible states). Graph transformation systems are one of the most widely used formal modeling systems and a suitable solution for modeling and checking complex systems. In systems where security property verification is not possible, the security feature can be refuted by searching for an accessible mode in which a specific configuration (e.g. error or undesirable behavior) occurs. Recent studies advocate that partial and intelligent exploration of part of the state space could be a good solution to the problem of state space explosion. The goal of this study is to use the random forest algorithm in the model checking which can solve the problem of state space explosion by selecting a few promising paths. A path is hopeful whenever the probability of reaching an answer through this path is higher than other paths. In the proposed method, a small model of the system is first created using the official language of the Graph Description System (GTS). Afterwards, a training data set of paths to the goal is generated from the small model mode space. The generated training data set is then provided to the random forest algorithm to identify and discover the logical relationships within it. In the next stage, the acquired knowledge is used to intelligently explore the incomplete space of the large model state. The proposed approach is used in the verification of the reachability property and to refute the safety feature in large and complex systems where it is impossible to generate the entire system state space. In order to evaluate the proposed approach, it has been implemented in GROOVE which is an open source tool for designing and checking models in graph conversion systems. The results indicate that the proposed method performs better than the compared methods in terms of average running time and the length of the generated witness. 153 166 2020/12/162019/08/152020/08/92019/09/282019/07/152019/07/262019/10/152019/09/272020/08/92019/12/52019/07/42019/09/27 1398/7/5 2021/05/222021/01/102021/01/102020/08/182020/02/32021/05/232021/12/62020/11/212022/01/152020/08/182021/06/262021/03/1 1399/12/11 جعفر پرتابیان jaafar partabian دانشگاه آزاد اسلامی، واحد یاسوج ایران Jaafar_partabian@yahoo.com وحید رافع vahid rafe دانشگاه اراک ایران v-rafe@araku.ac.ir حمید پروین hamid parvin دانشگاه آزاد اسلامی، واحد نورآباد ممسنی ایران parvinhamid@gmail.com صمد نجاتیان samad nejatian دانشگاه آزاد اسلامی، واحد یاسوج ایران samad.nej.2007@gmail.com کرم‌اله باقری‌فرد Karamollah bagherifard دانشگاه آزاد اسلامی، واحد یاسوج ایران k.bagheri@iauyasooj.ac.ir Software systems verification Knowledge discovery State space explosion intelligent search وارسی مدل تأیید سامانه‌های نرم‌افزاری کشف دانش انفجار فضای حالت جستجوی هوشمند [1] H. Zhang, J. Du, L. Cao and G. Zhu, "A full symbolic reachability analysis algorithm of timed automata based on BDD", In Autonomous Decentralized Systems (ISADS), IEEE Twelfth International Symposium, pp. 301-304, 2015.##[2] A. L. Lafuente, "Symmetry reduction and heuristic search for error detection in model checking", Workshop on Model Checking and Artificial Intelligence, 2003.##[3] A. L. Lafuente, S. Edelkamp, and S. Leue, "Partial order reduction in directed model checking", International SPIN Workshop on Model Checking of Software, Springer Berlin Heidelberg, pp. 112-127, 2002.##[4] V. Rafe, "Scenario-driven analysis of systems specified through graph transformations", Journal of Visual Languages & Computing, vol. 24, no. 2, pp. 136-145, 2013.##[5] A. Rensink and E. Zambon, "Pattern-based graph abstraction in Graph transformations", Springer Berlin Heidelberg, pp 66-80, 2012.##[6] S. Edelkamp, A. L. Lafuente, and S. Leue, "Protocol verification with heuristic search", AAAI Symposium on Model based Validation of Intelligence, 2001.##[7] S. Edelkamp, A. L. Lafuente, and S. Leue, "Directed explicit model checking with HSF-SPIN", Proceedings of the 8th international SPIN workshop on Model checking of software, pp. 57-79,2001.##[8] S. Edelkamp and F. Reffel, "OBDDs in heuristic search", Annual Conference on Artificial Intelligence, Springer Berlin Heidelberg, pp. 81-92, 1998.##[9] J. Maeoka, Y. Tanabe, and F. Ishikawa, "Depth-First Heuristic Search for Software Model Checking", In Computer and Information Science, Springer International Publishing, pp. 75-96, 2016.##[10] K. Havelund, T. Pressburger, "Model checking JAVA programs using JAVA Path Finder", International Journal on Software Tools for Technology Transfer, vol. 2, pp. 366-381,2000##[11] H. C. Estler and H. Wehrheim, "Heuristic search-based planning for graph transformation systems", KEPS 2011, pp. 54-66, 2011.##[12] E. Snippe, "Using heuristic search to solve planning problems in GROOVE", In 14th Twente Student Conference on IT, University of Twente, 2011.##[13] S. Ziegert, "Graph Transformation Planning via Abstraction", arXiv preprint arXiv: 1407.7933. 2014.##[14] J. W. Elsinga, "On a framework for domain independent heuristics in graph transformation planning", Master's thesis, University of Twente, 2016.##[15] P. Godefroid and S. Khurshid, "Exploring very large state spaces using genetic algorithms", International Conference on Tools and Algorithms for the Construction and Analysis of Systems, Springer Berlin Heidelberg, pp. 266-280, 2002.##[16] P. Godefroid, "Software Model Checking: The VeriSoft Approach Patrice Godefroid, Formal Methods in System Design". vol.26, pp. 77-101, 2005##[17] R. Yousefian, V. Rafe, M. Rahmani, "a heuristic approach for model checking graph transformation systems", Appl. Soft Compute, Vol. 24, pp. 169-180, 2014##[18] X. He, Z. Ma, W. Shao, G. Li, "A meta model for the notation of graphical modeling languages", In Computer Software and Applications Conference, COMPSAC 2007. 31st Annual International, IEEE, vol. 1, pp. 219-224, 2007.##[19] GROOVE, groove.sourceforge.net/groove-index.html##[20] R. Yousefian, S. Aboutorabi, and V. Rafe, "A greedy algorithm versus meta heuristic solutions to deadlock detection in Graph Transformation Systems", Journal of Intelligent & Fuzzy Systems, vol. 13, no. 1, pp. 1-13, 2016.##[21] E. Alba, F. Chicano, M. Ferreira, and J. Gomez-Pulido, "finding deadlocks in large concurrent java programs using genetic algorithms", 10th annual conference on Genetic and evolutionary computation, pp. 1735-1742, 2008.##[22] L. M. Duarte, L. Foss, R. Wagner, and T. Heimfarth, "Model Checking the Ant Colony Optimization, Distributed, Parallel and Biologically Inspired Systems IFIP Advances'', Information and Communication Technology, vol. 329, pp. 221-232, 2010##[23] B. L. Webster, "solving combinatorial optimization problems using a new algorithm based on gravitational attraction", Florida Institute of Technology, 2004.##[24] R. Behjati, M. Sirjani, and M. N. Ahmadabadi, "Bounded Rational Search for On-the-Fly Model Checking of LTL Properties", Fundamentals of Software Engineering, vol. 5961, pp. 292-307, 2010##[25] G.J. Holzmann, "On-The-Fly Model Checking", ACM Comput Surv, vol.28(4es), pp.120, 1996##[26] E. Pira, V. Rafe, A. Nikanjam, "EMCDM: efficient model checking by data mining for verification of complex software systems specified through architectural styles", Appl. Soft Compute, Vol. 44pp, pp.1185-1201, 2016.##[27] E. Pira, V. Rafe, A. Nikanjam, "Deadlock detection in complex software systems specified through graph transformation using Bayesian optimization algorithm", Journal of System and Software, vol. 131, pp. 181-200, 2017##[28] J. Partabian, V. Rafe, H. Parvin, S. Nejatian, "An Approach Based on Knowledge Exploration for State Space Management in Checking Reachability of Complex Software Systems", soft computing, vol. 24, pp.1-16, 2019.##[29] M. Yasrebi, V. Rafe, H. Parvin, S. Nejatian, "An efficient approach to state space management in model checking of complex software system using machine learning technique", journal of intelligent & Fuzzy system, vol.38, no. 2, pp. 1761-1773, 2020.##[30] G. Rozenberg, "Handbook of Graph Grammars and Comp", World scientific, vol. 1, 1997.##[31] H. Kastenberg and A. Rensink, "Model Checking Dynamic States in GROOVE", International SPIN Workshop on Model Checking of Software, Springer Berlin Heidelberg, pp. 299-305, 2006.##[32] A. Groce and W. Visser, "Heuristics for model checking Java programs", International Journal on Software Tools for Technology Transfer (STTT), vol. 6, no. 4, pp. 260-276, 2004.##[33] S. Edelkamp, A. L. Lafuente, and S. Leue, "Protocol verification with heuristic search", In AAAI Symposium on Model-based Validation of Intelligence, pp. 75-83, 2001.##[34] V. Rafe, M. Moradi, R. Yousefian, and A. Nikanjam, "A Meta-Heuristic Approach for Automated Refutation of Complex Software Systems Specified through Graph Transformations", Applied Soft Computing, vol. 33, pp. 136-149, 2015.##[1] H. Zhang, J. Du, L. Cao and G. Zhu, "A full symbolic reachability analysis algorithm of timed automata based on BDD", In Autonomous Decentralized Systems (ISADS), IEEE Twelfth International Symposium, pp. 301-304, 2015.##[2] A. L. Lafuente, "Symmetry reduction and heuristic search for error detection in model checking", Workshop on Model Checking and Artificial Intelligence, 2003.##[3] A. L. Lafuente, S. Edelkamp, and S. Leue, "Partial order reduction in directed model checking", International SPIN Workshop on Model Checking of Software, Springer Berlin Heidelberg, pp. 112-127, 2002.##[4] V. Rafe, "Scenario-driven analysis of systems specified through graph transformations", Journal of Visual Languages & Computing, vol. 24, no. 2, pp. 136-145, 2013.##[5] A. Rensink and E. Zambon, "Pattern-based graph abstraction in Graph transformations", Springer Berlin Heidelberg, pp 66-80, 2012.##[6] S. Edelkamp, A. L. Lafuente, and S. Leue, "Protocol verification with heuristic search", AAAI Symposium on Model based Validation of Intelligence, 2001.##[7] S. Edelkamp, A. L. Lafuente, and S. Leue, "Directed explicit model checking with HSF-SPIN", Proceedings of the 8th international SPIN workshop on Model checking of software, pp. 57-79,2001.##[8] S. Edelkamp and F. Reffel, "OBDDs in heuristic search", Annual Conference on Artificial Intelligence, Springer Berlin Heidelberg, pp. 81-92, 1998.##[9] J. Maeoka, Y. Tanabe, and F. Ishikawa, "Depth-First Heuristic Search for Software Model Checking", In Computer and Information Science, Springer International Publishing, pp. 75-96, 2016.##[10] K. Havelund, T. Pressburger, "Model checking JAVA programs using JAVA Path Finder", International Journal on Software Tools for Technology Transfer, vol. 2, pp. 366-381,2000##[11] H. C. Estler and H. Wehrheim, "Heuristic search-based planning for graph transformation systems", KEPS 2011, pp. 54-66, 2011.##[12] E. Snippe, "Using heuristic search to solve planning problems in GROOVE", In 14th Twente Student Conference on IT, University of Twente, 2011.##[13] S. Ziegert, "Graph Transformation Planning via Abstraction", arXiv preprint arXiv: 1407.7933. 2014.##[14] J. W. Elsinga, "On a framework for domain independent heuristics in graph transformation planning", Master's thesis, University of Twente, 2016.##[15] P. Godefroid and S. Khurshid, "Exploring very large state spaces using genetic algorithms", International Conference on Tools and Algorithms for the Construction and Analysis of Systems, Springer Berlin Heidelberg, pp. 266-280, 2002.##[16] P. Godefroid, "Software Model Checking: The VeriSoft Approach Patrice Godefroid, Formal Methods in System Design". vol.26, pp. 77-101, 2005##[17] R. Yousefian, V. Rafe, M. Rahmani, "a heuristic approach for model checking graph transformation systems", Appl. Soft Compute, Vol. 24, pp. 169-180, 2014##[18] X. He, Z. Ma, W. Shao, G. Li, "A meta model for the notation of graphical modeling languages", In Computer Software and Applications Conference, COMPSAC 2007. 31st Annual International, IEEE, vol. 1, pp. 219-224, 2007.##[19] GROOVE, groove.sourceforge.net/groove-index.html##[20] R. Yousefian, S. Aboutorabi, and V. Rafe, "A greedy algorithm versus meta heuristic solutions to deadlock detection in Graph Transformation Systems", Journal of Intelligent & Fuzzy Systems, vol. 13, no. 1, pp. 1-13, 2016.##[21] E. Alba, F. Chicano, M. Ferreira, and J. Gomez-Pulido, "finding deadlocks in large concurrent java programs using genetic algorithms", 10th annual conference on Genetic and evolutionary computation, pp. 1735-1742, 2008.##[22] L. M. Duarte, L. Foss, R. Wagner, and T. Heimfarth, "Model Checking the Ant Colony Optimization, Distributed, Parallel and Biologically Inspired Systems IFIP Advances'', Information and Communication Technology, vol. 329, pp. 221-232, 2010##[23] B. L. Webster, "solving combinatorial optimization problems using a new algorithm based on gravitational attraction", Florida Institute of Technology, 2004.##[24] R. Behjati, M. Sirjani, and M. N. Ahmadabadi, "Bounded Rational Search for On-the-Fly Model Checking of LTL Properties", Fundamentals of Software Engineering, vol. 5961, pp. 292-307, 2010##[25] G.J. Holzmann, "On-The-Fly Model Checking", ACM Comput Surv, vol.28(4es), pp.120, 1996##[26] E. Pira, V. Rafe, A. Nikanjam, "EMCDM: efficient model checking by data mining for verification of complex software systems specified through architectural styles", Appl. Soft Compute, Vol. 44pp, pp.1185-1201, 2016.##[27] E. Pira, V. Rafe, A. Nikanjam, "Deadlock detection in complex software systems specified through graph transformation using Bayesian optimization algorithm", Journal of System and Software, vol. 131, pp. 181-200, 2017##[28] J. Partabian, V. Rafe, H. Parvin, S. Nejatian, "An Approach Based on Knowledge Exploration for State Space Management in Checking Reachability of Complex Software Systems", soft computing, vol. 24, pp.1-16, 2019.##[29] M. Yasrebi, V. Rafe, H. Parvin, S. Nejatian, "An efficient approach to state space management in model checking of complex software system using machine learning technique", journal of intelligent & Fuzzy system, vol.38, no. 2, pp. 1761-1773, 2020.##[30] G. Rozenberg, "Handbook of Graph Grammars and Comp", World scientific, vol. 1, 1997.##[31] H. Kastenberg and A. Rensink, "Model Checking Dynamic States in GROOVE", International SPIN Workshop on Model Checking of Software, Springer Berlin Heidelberg, pp. 299-305, 2006.##[32] A. Groce and W. Visser, "Heuristics for model checking Java programs", International Journal on Software Tools for Technology Transfer (STTT), vol. 6, no. 4, pp. 260-276, 2004.##[33] S. Edelkamp, A. L. Lafuente, and S. Leue, "Protocol verification with heuristic search", In AAAI Symposium on Model-based Validation of Intelligence, pp. 75-83, 2001.##[34] V. Rafe, M. Moradi, R. Yousefian, and A. Nikanjam, "A Meta-Heuristic Approach for Automated Refutation of Complex Software Systems Specified through Graph Transformations", Applied Soft Computing, vol. 33, pp. 136-149, 2015. ##